### 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)

tis: Iterator[TI] = with_row_locks(
            query,
            of=TI,
            session=session,
            **skip_locked(session=session),
        )
        for ti in tis:
            try_number = ti_primary_key_to_try_number_map[ti.key.primary]
            buffer_key = ti.key.with_try_number(try_number)
            state, info = event_buffer.pop(buffer_key)
            if state == State.QUEUED:
                ti.external_executor_id = info
                self.log.info("Setting external_id for %s to %s", ti, info)
                continue
            msg = (
                "TaskInstance Finished: dag_id=%s, task_id=%s, run_id=%s, map_index=%s, "
                "run_start_date=%s, run_end_date=%s, "
                "run_duration=%s, state=%s, executor_state=%s, try_number=%s, max_tries=%s, job_id=%s, "
                "pool=%s, queue=%s, priority_weight=%d, operator=%s, queued_dttm=%s, "
                "queued_by_job_id=%s, pid=%s"
            )
            self.log.info(
                msg,
                ti.dag_id,
                ti.task_id,
                ti.run_id,
                ti.map_index,
                ti.start_date,
                ti.end_date,
                ti.duration,