### 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)

and_(
                TR.dag_id == dag_id,
                or_(
                    and_(
                        TR.run_id == run_id,
                        or_(
                            and_(
                                TR.map_index == map_index,
                                or_(
                                    and_(TR.try_number == try_number, TR.task_id.in_(task_ids))
                                    for try_number, task_ids in task_tries.items()
                                ),
                            )
                            for map_index, task_tries in map_indexes.items()
                        ),
                    )
                    for run_id, map_indexes in run_ids.items()
                ),
            )
            for dag_id, run_ids in task_id_by_key.items()
        )
        delete_qry = TR.__table__.delete().where(conditions)
        session.execute(delete_qry)
    if job_ids:
        from airflow.jobs.job import Job
        for job in session.query(Job).filter(Job.id.in_(job_ids)).all():
            job.state = TaskInstanceState.RESTARTING
    if activate_dag_runs is not None:
        warnings.warn(
            "`activate_dag_runs` parameter to clear_task_instances function is deprecated. "