### 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)

)
            for task_instance in unfinished_task_instances:
                task_instance.state = TaskInstanceState.SKIPPED
                session.merge(task_instance)
            session.flush()
            self.log.info("Run %s of %s has timed-out", dag_run.run_id, dag_run.dag_id)
            active_runs = dag.get_num_active_runs(only_running=False, session=session)
            
            if self._should_update_dag_next_dagruns(dag, dag_model, active_runs):
                dag_model.calculate_dagrun_date_fields(dag, dag.get_run_data_interval(dag_run))
            callback_to_execute = DagCallbackRequest(
                full_filepath=dag.fileloc,
                dag_id=dag.dag_id,
                run_id=dag_run.run_id,
                is_failure_callback=True,
                processor_subdir=dag_model.processor_subdir,
                msg="timed_out",
            )
            dag_run.notify_dagrun_state_changed()
            duration = dag_run.end_date - dag_run.start_date
            Stats.timing(f"dagrun.duration.failed.{dag_run.dag_id}", duration)
            Stats.timing("dagrun.duration.failed", duration, tags={"dag_id": dag_run.dag_id})
            return callback_to_execute
        if dag_run.execution_date > timezone.utcnow() and not dag.allow_future_exec_dates:
            self.log.error("Execution date is in future: %s", dag_run.execution_date)
            return callback
        if not self._verify_integrity_if_dag_changed(dag_run=dag_run, session=session):
            self.log.warning("The DAG disappeared before verifying integrity: %s. Skipping.", dag_run.dag_id)
            return callback