### 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)

self, actual_start_date, reschedule_exception, test_mode=False, session=NEW_SESSION
    ):
        
        if test_mode:
            return
        from airflow.models.dagrun import DagRun  
        self.refresh_from_db(session)
        self.end_date = timezone.utcnow()
        self.set_duration()
        
        
        with_row_locks(
            session.query(DagRun).filter_by(
                dag_id=self.dag_id,
                run_id=self.run_id,
            ),
            session=session,
        ).one()
        
        session.add(
            TaskReschedule(
                self.task,
                self.run_id,
                self._try_number,
                actual_start_date,
                self.end_date,