### 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)

full_filepath=dag.fileloc,
                    dag_id=self.dag_id,
                    run_id=self.run_id,
                    is_failure_callback=False,
                    processor_subdir=None if dag_model is None else dag_model.processor_subdir,
                    msg="success",
                )
        
        elif unfinished.should_schedule and not are_runnable_tasks:
            self.log.error("Task deadlock (no runnable tasks); marking run %s failed", self)
            self.set_state(DagRunState.FAILED)
            self.notify_dagrun_state_changed(msg="all_tasks_deadlocked")
            if execute_callbacks:
                dag.handle_callback(self, success=False, reason="all_tasks_deadlocked", session=session)
            elif dag.has_on_failure_callback:
                from airflow.models.dag import DagModel
                dag_model = DagModel.get_dagmodel(dag.dag_id, session)
                callback = DagCallbackRequest(
                    full_filepath=dag.fileloc,
                    dag_id=self.dag_id,
                    run_id=self.run_id,
                    is_failure_callback=True,
                    processor_subdir=None if dag_model is None else dag_model.processor_subdir,
                    msg="all_tasks_deadlocked",
                )
        
        else:
            self.set_state(DagRunState.RUNNING)
        if self._state == DagRunState.FAILED or self._state == DagRunState.SUCCESS:
            msg = (