### 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)

"""
        Reloads the current dagrun from the database
        :param session: database session
        """
        dr = session.query(DagRun).filter(DagRun.dag_id == self.dag_id, DagRun.run_id == self.run_id).one()
        self.id = dr.id
        self.state = dr.state
    @classmethod
    @provide_session
    def active_runs_of_dags(
        cls,
        dag_ids: Iterable[str] | None = None,
        only_running: bool = False,
        session: Session = NEW_SESSION,
    ) -> dict[str, int]:
        """Get the number of active dag runs for each dag."""
        query = session.query(cls.dag_id, func.count("*"))
        if dag_ids is not None:
            
            
            query = query.filter(cls.dag_id.in_(set(dag_ids)))
        if only_running:
            query = query.filter(cls.state == State.RUNNING)
        else:
            query = query.filter(cls.state.in_([State.RUNNING, State.QUEUED]))
        query = query.group_by(cls.dag_id)
        return {dag_id: count for dag_id, count in query.all()}
    @classmethod
    def next_dagruns_to_examine(