Current Implementation:
=======================
The current implementation of _get_l10n_in_gstr1_json relies on multiple iterations over ORM recordsets. Since the ORM loads multiple fields rather than only the required fields, memory consumption grows significantly for large datasets (around 700 MB for 150K account move lines).

Additionally, the method builds a single large dictionary from _get_tax_details that is tailored for the Indian GST reporting logic. Constructing and holding this intermediate data structure further increases memory usage.

The combination of repeated Python loops, ORM overhead, and the large intermediate dictionary results in high execution time and memory consumption, causing the process to exceed the available time and memory limits for large exports.

Solution:
=========
Instead of processing tax details through the ORM, create a temporary table containing the GST tax details and query this subset directly for each GSTR-1 subsection.

This approach bypasses the ORM, fetching only the required columns instead of entire records. Eliminates the need to build the large _get_tax_details dictionary. Reduces the number of Python-side iterations and intermediate data structures. Pushes the data aggregation and filtering to SQL, where it is more efficient. Restricts Python's responsibility to formatting the final JSON output.

This significantly reduces memory usage, improves execution speed, and makes the implementation simpler and easier to maintain.

Benchmark Performance Results:
==============================
```text
+-----------+-------------+----------------------+------------------+
| Size      | Parameter   | Current Version      | IMPROVED SQL +   |
|           |             |                      | Indian Reports   |
+-----------+-------------+----------------------+------------------+
| 150K AMLs | Time        | 28.19s               | 2.18s            |
|           +-------------+----------------------+------------------+
|           | Peak Memory | 711 MB               | 34.7 MB          |
+-----------+-------------+----------------------+------------------+
| 250K AMLs | Time        | 64s                  | 3.68s            |
|           +-------------+----------------------+------------------+
|           | Peak Memory | 1.4 GB               | 58.5 MB          |
+-----------+-------------+----------------------+------------------+
| 500K AMLs | Time        | 208s                 | 7.33s            |
|           +-------------+----------------------+------------------+
|           | Peak Memory | 2.7 GB               | 114.8 MB         |
+-----------+-------------+----------------------+------------------+
| 1M AMLs   | Time        | Memory Limit (7 GB+) | 15.07s           |
|           +-------------+----------------------+------------------+
|           | Peak Memory | Memory Limit (7 GB+) | 230.7 MB         |
+-----------+-------------+----------------------+------------------+
| 2M AMLs   | Time        | Memory Limit (7 GB+) | 28.20s           |
|           +-------------+----------------------+------------------+
|           | Peak Memory | Memory Limit (7 GB+) | 461.4 MB         |
+-----------+-------------+----------------------+------------------+
```

ref - https://drive.google.com/drive/folders/10I8gJmGgZVAMW8te_GFg5lleTUW1Nku1?usp=drive_link

--------------------------

task-3941950

Community PR - https://github.com/odoo/odoo/pull/274381