### 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)

session.query(cls)
            .with_hint(cls, "USE INDEX (idx_dag_run_running_dags)", dialect_name="mysql")
            .filter(cls.state == state, cls.run_type != DagRunType.BACKFILL_JOB)
            .join(DagModel, DagModel.dag_id == cls.dag_id)
            .filter(DagModel.is_paused == false(), DagModel.is_active == true())
        )
        if state == State.QUEUED:
            
            
            running_drs = (
                session.query(DagRun.dag_id, func.count(DagRun.state).label("num_running"))
                .filter(DagRun.state == DagRunState.RUNNING)
                .group_by(DagRun.dag_id)
                .subquery()
            )
            query = query.outerjoin(running_drs, running_drs.c.dag_id == DagRun.dag_id).filter(
                func.coalesce(running_drs.c.num_running, 0) < DagModel.max_active_runs
            )
        query = query.order_by(
            nulls_first(cls.last_scheduling_decision, session=session),
            cls.execution_date,
        )
        if not settings.ALLOW_FUTURE_EXEC_DATES:
            query = query.filter(DagRun.execution_date <= func.now())
        return with_row_locks(
            query.limit(max_number), of=cls, session=session, **skip_locked(session=session)
        )
    @classmethod
    @provide_session
    def find(