### 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)

.join(TI.dag_run)
                .filter(DR.run_type != DagRunType.BACKFILL_JOB, DR.state == DagRunState.RUNNING)
                .join(TI.dag_model)
                .filter(not_(DM.is_paused))
                .filter(TI.state == TaskInstanceState.SCHEDULED)
                .options(selectinload("dag_model"))
                .order_by(-TI.priority_weight, DR.execution_date, TI.map_index)
            )
            if starved_pools:
                query = query.filter(not_(TI.pool.in_(starved_pools)))
            if starved_dags:
                query = query.filter(not_(TI.dag_id.in_(starved_dags)))
            if starved_tasks:
                task_filter = tuple_in_condition((TI.dag_id, TI.task_id), starved_tasks)
                query = query.filter(not_(task_filter))
            if starved_tasks_task_dagrun_concurrency:
                task_filter = tuple_in_condition(
                    (TI.dag_id, TI.run_id, TI.task_id),
                    starved_tasks_task_dagrun_concurrency,
                )
                query = query.filter(not_(task_filter))
            query = query.limit(max_tis)
            timer = Stats.timer("scheduler.critical_section_query_duration")
            timer.start()
            try:
                task_instances_to_examine: list[TI] = with_row_locks(
                    query,
                    of=TI,
                    session=session,
                    **skip_locked(session=session),