### 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)

job_id: str | None = None,
        pool: str | None = None,
        session: Session = NEW_SESSION,
    ) -> TaskReturnCode | None:
        """
        Immediately runs the task (without checking or changing db state
        before execution) and then sets the appropriate final state after
        completion and runs any post-execute callbacks. Meant to be called
        only after another function changes the state to running.
        :param mark_success: Don't run the task, mark its state as success
        :param test_mode: Doesn't record success or failure in the DB
        :param pool: specifies the pool to use to run the task instance
        :param session: SQLAlchemy ORM Session
        """
        self.test_mode = test_mode
        self.refresh_from_task(self.task, pool_override=pool)
        self.refresh_from_db(session=session)
        self.job_id = job_id
        self.hostname = get_hostname()
        self.pid = os.getpid()
        if not test_mode:
            session.merge(self)
            session.commit()
        actual_start_date = timezone.utcnow()
        Stats.incr(f"ti.start.{self.task.dag_id}.{self.task.task_id}", tags=self.stats_tags)
        
        for state in State.task_states:
            Stats.incr(
                f"ti.finish.{self.task.dag_id}.{self.task.task_id}.{state}",
                count=0,