### Description

expanding tasks in batches to allow mapped tasks spawn more than 1024 processes.

### Use case/motivation

Maximum length of a list is limited to 1024 by `max_map_length (AIRFLOW__CORE__MAX_MAP_LENGTH)`.
during scheduling of the new tasks, an UPDATE query is ran that tries to set all the new tasks at once.  Increasing `max_map_length` more than 4K makes airflow scheduler completely unresponsive.

Also, Postgres throws `stack depth limit exceeded` error which can be fixed by updating to a newer version and setting `max_stack_depth` higher. But it doesn't really matter because airflow scheduler freezes up.
 
As a workaround, I split the dag runs into subdag runs which works but it would be much nicer if we didn't have to worry about exceeding  `max_map_length`. 

### Related issues

It was discussed here:
[Increasing 'max_map_length' leads to SQL 'max_stack_depth' error with 5000 dags to be spawned #28478](https://github.com/apache/airflow/discussions/28478)

### Are you willing to submit a PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

if dag_ids == {dag_id} and run_ids == {run_id} and map_indices == {map_index}:
            return and_(
                TaskInstance.dag_id == dag_id,
                TaskInstance.run_id == run_id,
                TaskInstance.map_index == map_index,
                TaskInstance.task_id.in_(task_ids),
            )
        if dag_ids == {dag_id} and task_ids == {first_task_id} and map_indices == {map_index}:
            return and_(
                TaskInstance.dag_id == dag_id,
                TaskInstance.run_id.in_(run_ids),
                TaskInstance.map_index == map_index,
                TaskInstance.task_id == first_task_id,
            )
        if dag_ids == {dag_id} and run_ids == {run_id} and task_ids == {first_task_id}:
            return and_(
                TaskInstance.dag_id == dag_id,
                TaskInstance.run_id == run_id,
                TaskInstance.map_index.in_(map_indices),
                TaskInstance.task_id == first_task_id,
            )
        filter_condition = []
        
        
        task_id_groups: dict[tuple, dict[Any, list[Any]]] = defaultdict(lambda: defaultdict(list))
        map_index_groups: dict[tuple, dict[Any, list[Any]]] = defaultdict(lambda: defaultdict(list))
        for t in tis:
            task_id_groups[(t.dag_id, t.run_id)][t.task_id].append(t.map_index)
            map_index_groups[(t.dag_id, t.run_id)][t.map_index].append(t.task_id)