**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