### 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 DagRuns in queued state and decide moving them to running state."""
        dag_runs = self._get_next_dagruns_to_examine(DagRunState.QUEUED, session)
        active_runs_of_dags = Counter(
            DagRun.active_runs_of_dags((dr.dag_id for dr in dag_runs), only_running=True, session=session),
        )
        def _update_state(dag: DAG, dag_run: DagRun):
            dag_run.state = DagRunState.RUNNING
            dag_run.start_date = timezone.utcnow()
            if dag.timetable.periodic:
                
                
                
                
                
                expected_start_date = dag.get_run_data_interval(dag_run).end
                schedule_delay = dag_run.start_date - expected_start_date
                
                Stats.timing(f"dagrun.schedule_delay.{dag.dag_id}", schedule_delay)
                Stats.timing(
                    "dagrun.schedule_delay",