### 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)

"""
        Mark tasks stuck in queued for longer than `task_queued_timeout` as failed.
        Tasks can get stuck in queued for a wide variety of reasons (e.g. celery loses
        track of a task, a cluster can't further scale up its workers, etc.), but tasks
        should not be stuck in queued for a long time. This will mark tasks stuck in
        queued for longer than `self._task_queued_timeout` as failed. If the task has
        available retries, it will be retried.
        """
        self.log.debug("Calling SchedulerJob._fail_tasks_stuck_in_queued method")
        tasks_stuck_in_queued = (
            session.query(TI)
            .filter(
                TI.state == State.QUEUED,
                TI.queued_dttm < (timezone.utcnow() - timedelta(seconds=self._task_queued_timeout)),
                TI.queued_by_job_id == self.job.id,
            )
            .all()
        )
        try:
            tis_for_warning_message = self.job.executor.cleanup_stuck_queued_tasks(tis=tasks_stuck_in_queued)
            if tis_for_warning_message:
                task_instance_str = "\n\t".join(tis_for_warning_message)
                self.log.warning(
                    "Marked the following %s task instances stuck in queued as failed. "
                    "If the task instance has available retries, it will be retried.\n\t%s",
                    len(tasks_stuck_in_queued),
                    task_instance_str,
                )
        except NotImplementedError:
            self.log.debug("Executor doesn't support cleanup of stuck queued tasks. Skipping.")