As the system now starts to be used by real users, we noticed a few points of improvement that can easily be done in stable and will provide a better UX when interacting with the system.

The payment proof as a field was a consequence of multiple iterations of the system; but it ended up only as a way to input the proof and nothing else.
It is confusing, only allows one proof at a time, and is overall not nice to use.
To improve that, we will remove the field and replace it with an 'Attach Payment Proof' button similar to the expense app, allowing for a better experience.

The similar rental check was checking even if all the related fields were empty. On a database migrating from a previous version, this leads to ALL the rentals to be marked as similar, which isn't ideal.
The same check was also comparing multiple rentals from the same employee as long as they are active. As rentals for an employee cannot overlap, it makes no sense to check this case and cause false positives.

task-6448054

Forward-Port-Of: odoo/enterprise#126764

In this commit:
------------------
- Maintain separate Fiskaly transaction flows for retail (short tx) and restaurant (long tx)
  orders as discussed with the Fiskaly team.
- `Initialize order transactions` with an empty payload when the `first product` is added.
- Start `receipt transactions` with an empty payload when the `first payment line` is added.
- For retail flows, no intermediate order updates are sent to Fiskaly before finalization.
- For restaurant flows, create additional transaction updates during kitchen synchronization.
  Ensure already synchronized products are not resent, and only newly added or updated
  quantities are included in the payload.
- `Finalize order and receipt transactions` with complete order lines and payment details
  when we validate the order.

task: 6208963

Reference:
<img width="1863" height="1285" alt="de_tss_flow" src="https://github.com/user-attachments/assets/9140788e-7948-4a08-9f11-27197b22ca8b" />

Forward-Port-Of: odoo/enterprise#120529
Forward-Port-Of: odoo/enterprise#117526

**Description:**
While loading the budget report, the bad queries are created by 
```def _get_aal_query()``` and ```def _get_pol_query()``` function, makes 
the budget report unusable.

**Root cause:**
Instead of doing a hash join while searching the record,
 the OR statement in the Left Join in the condition 
```(%(bl)s IS NULL OR %(a)s = %(bl)s)``` 
creates a nested for loop that compares everything single aal to bl, 
this causes a significant performance issue as the number of the 
number of check will be the the number aal * bl,
if a database has a 70k aal and 20k bl, both numbers are not large
but it will cause a 70k * 20k search which is more than a billion.

**Fix**:
There are some refactors made in this PR.

_First_, separate out the Q1.
In order to find the aal that has no bl connects to it. 
Doing a search to find the aals that have bl and then subtract them from all aals.

_Second_, Instead of doing a nested loop for by using
 ```(%(bl)s IS NULL OR %(a)s = %(bl)s)```, 
originally we will have do something like
```
JOIN budget_line bl
  ON (bl.x_plan2_id IS NULL OR aal.x_plan2_id = bl.x_plan2_id)
 AND (bl.x_plan3_id IS NULL OR aal.x_plan3_id = bl.x_plan3_id)
 AND (bl.x_plan4_id IS NULL OR aal.x_plan4_id = bl.x_plan4_id)
```
Assuming each bl has three plans ```x_plan2_id```, ```x_plan3_id```, ```x_plan4_id```

Grouping the bl base on whether a specific plan is set, (i.e. shapes)
we can skip the ```IS NULL OR``` because we already know which plan
 is null and do the hash join directly.

For example, the shapes will be a dictionary with a key of a tuple of booleans
based on whether a plan is set or not and the value is a list of bl_id.

```
{
    (True, False, False): [1, 2],
    (False, True, True):  [3, 4],
    (False, False, False): [5],
}
```

we can end up doing something like

```
JOIN budget_line bl
 ON bl.id = ANY(ARRAY[3,4])
 AND aal.x_plan3_id = bl.x_plan3_id AND aal.x_plan4_id = bl.x_plan4_id
```
which is way more faster.

---
The benchmark is made locally from this client's database which contains
 69k aal, 23k bl, 6829 pol and 3 plans for aal and bl.

|Record count                                      |Time before|Time after|
|--------------------------------------------------|-----------------|---------------|
|69k aal, 23k bl, 6829 pol, 3 plans    |70.04s         |4.6s            |

Dalibo:

Before: 
Month-over-month grand total by company:
https://explain.dalibo.com/plan/8h3d4e89aaf9f3d4
Overall grand total by company:
https://explain.dalibo.com/plan/445g1f9caf4923e2
Month-over-month grand total by plan: 
https://explain.dalibo.com/plan/53a138ca50b2a7c4
Overall grand total by plan: 
https://explain.dalibo.com/plan/hdbe169ddc7g5785

After: 
Month-over-month grand total by company:
https://explain.dalibo.com/plan/hcc86c801e6872bf
Overall grand total by company:
https://explain.dalibo.com/plan/69b2421a3581f98h
Month-over-month grand total by plan:
https://explain.dalibo.com/plan/a88f398bbbch3148
Overall grand total by plan: 
https://explain.dalibo.com/plan/1gg749ae7ab1553c

opw-6345552