### 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)

"Please use `dag_run_state`",
            RemovedInAirflow3Warning,
            stacklevel=2,
        )
        if not activate_dag_runs:
            dag_run_state = False
    if dag_run_state is not False and tis:
        from airflow.models.dagrun import DagRun  
        run_ids_by_dag_id = defaultdict(set)
        for instance in tis:
            run_ids_by_dag_id[instance.dag_id].add(instance.run_id)
        drs = (
            session.query(DagRun)
            .filter(
                or_(
                    and_(DagRun.dag_id == dag_id, DagRun.run_id.in_(run_ids))
                    for dag_id, run_ids in run_ids_by_dag_id.items()
                )
            )
            .all()
        )
        dag_run_state = DagRunState(dag_run_state)  
        for dr in drs:
            dr.state = dag_run_state
            dr.start_date = timezone.utcnow()
            if dag_run_state == DagRunState.QUEUED:
                dr.last_scheduling_decision = None
                dr.start_date = None
    session.flush()
def _is_mappable_value(value: Any) -> TypeGuard[Collection]: