### 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)

"""
        Returns whether a task is in UP_FOR_RETRY state and its retry interval
        has elapsed.
        """
        
        return self.state == State.UP_FOR_RETRY and not self.ready_for_retry()
    @provide_session
    def are_dependents_done(self, session: Session = NEW_SESSION) -> bool:
        """
        Checks whether the immediate dependents of this task instance have succeeded or have been skipped.
        This is meant to be used by wait_for_downstream.
        This is useful when you do not want to start processing the next
        schedule of a task until the dependents are done. For instance,
        if the task DROPs and recreates a table.
        :param session: SQLAlchemy ORM Session
        """
        task = self.task
        if not task.downstream_task_ids:
            return True
        ti = session.query(func.count(TaskInstance.task_id)).filter(
            TaskInstance.dag_id == self.dag_id,
            TaskInstance.task_id.in_(task.downstream_task_ids),
            TaskInstance.run_id == self.run_id,
            TaskInstance.state.in_([State.SKIPPED, State.SUCCESS]),
        )
        count = ti[0][0]
        return count == len(task.downstream_task_ids)
    @provide_session
    def get_previous_dagrun(