### 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)

return self.run_type == DagRunType.BACKFILL_JOB
    @classmethod
    @provide_session
    def get_latest_runs(cls, session: Session = NEW_SESSION) -> list[DagRun]:
        """Returns the latest DagRun for each DAG"""
        subquery = (
            session.query(cls.dag_id, func.max(cls.execution_date).label("execution_date"))
            .group_by(cls.dag_id)
            .subquery()
        )
        return (
            session.query(cls)
            .join(
                subquery,
                and_(cls.dag_id == subquery.c.dag_id, cls.execution_date == subquery.c.execution_date),
            )
            .all()
        )
    @provide_session
    def schedule_tis(self, schedulable_tis: Iterable[TI], session: Session = NEW_SESSION) -> int: