### 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)

DagRun.dag_id == dag.dag_id,
                        DagRun.execution_date < exec_date,
                        DagRun.run_type == DagRunType.DATASET_TRIGGERED,
                    )
                    .order_by(DagRun.execution_date.desc())
                    .first()
                )
                dataset_event_filters = [
                    DagScheduleDatasetReference.dag_id == dag.dag_id,
                    DatasetEvent.timestamp <= exec_date,
                ]
                if previous_dag_run:
                    dataset_event_filters.append(DatasetEvent.timestamp > previous_dag_run.execution_date)
                dataset_events = (
                    session.query(DatasetEvent)
                    .join(
                        DagScheduleDatasetReference,
                        DatasetEvent.dataset_id == DagScheduleDatasetReference.dataset_id,
                    )
                    .join(DatasetEvent.source_dag_run)
                    .filter(*dataset_event_filters)
                    .all()
                )
                data_interval = dag.timetable.data_interval_for_events(exec_date, dataset_events)
                run_id = dag.timetable.generate_run_id(
                    run_type=DagRunType.DATASET_TRIGGERED,
                    logical_date=exec_date,
                    data_interval=data_interval,
                    session=session,
                    events=dataset_events,