### 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)

"""
        Find zombie task instances, which are tasks haven't heartbeated for too long
        or have a no-longer-running LocalTaskJob, and create a TaskCallbackRequest
        to be handled by the DAG processor.
        """
        from airflow.jobs.job import Job
        self.log.debug("Finding 'running' jobs without a recent heartbeat")
        limit_dttm = timezone.utcnow() - timedelta(seconds=self._zombie_threshold_secs)
        with create_session() as session:
            zombies: list[tuple[TI, str, str]] = (
                session.query(TI, DM.fileloc, DM.processor_subdir)
                .with_hint(TI, "USE INDEX (ti_state)", dialect_name="mysql")
                .join(Job, TI.job_id == Job.id)
                .join(DM, TI.dag_id == DM.dag_id)
                .filter(TI.state == TaskInstanceState.RUNNING)
                .filter(
                    or_(
                        Job.state != State.RUNNING,
                        Job.latest_heartbeat < limit_dttm,
                    )
                )
                .filter(Job.job_type == "LocalTaskJob")
                .filter(TI.queued_by_job_id == self.job.id)
                .all()