### 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)

execution_date = get_prev_execution_date()
            if execution_date is None:
                return None
            return execution_date.strftime(r"%Y-%m-%d")
        def get_prev_ds_nodash() -> str | None:
            prev_ds = get_prev_ds()
            if prev_ds is None:
                return None
            return prev_ds.replace("-", "")
        def get_triggering_events() -> dict[str, list[DatasetEvent]]:
            if TYPE_CHECKING:
                assert session is not None
            
            
            
            nonlocal dag_run
            if dag_run not in session:
                dag_run = session.merge(dag_run, load=False)
            dataset_events = dag_run.consumed_dataset_events
            triggering_events: dict[str, list[DatasetEvent]] = defaultdict(list)
            for event in dataset_events:
                triggering_events[event.dataset.uri].append(event)
            return triggering_events