### 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 is_done or not found_new_filters:
                break
            self.log.info(
                "Found no task instances to queue on query iteration %s "
                "but there could be more candidate task instances to check.",
                loop_count,
            )
        for pool_name, num_starving_tasks in pool_num_starving_tasks.items():
            Stats.gauge(f"pool.starving_tasks.{pool_name}", num_starving_tasks)
        Stats.gauge("scheduler.tasks.starving", num_starving_tasks_total)
        Stats.gauge("scheduler.tasks.executable", len(executable_tis))
        if len(executable_tis) > 0:
            task_instance_str = "\n\t".join(repr(x) for x in executable_tis)
            self.log.info("Setting the following tasks to queued state:\n\t%s", task_instance_str)
            
            filter_for_tis = TI.filter_for_tis(executable_tis)
            session.query(TI).filter(filter_for_tis).update(
                
                
                {
                    TI.state: TaskInstanceState.QUEUED,
                    TI.queued_dttm: timezone.utcnow(),
                    TI.queued_by_job_id: self.job.id,
                },
                synchronize_session=False,
            )
        for ti in executable_tis:
            make_transient(ti)
        return executable_tis
    def _enqueue_task_instances_with_queued_state(self, task_instances: list[TI], session: Session) -> None: