### 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)

"""
        Handle case where self.task_runner exits by itself or is externally killed.
        Don't run any callbacks.
        """
        
        self.terminating = True
        self._log_return_code_metric(return_code)
        is_deferral = return_code == TaskReturnCode.DEFERRED.value
        if is_deferral:
            self.log.info("Task exited with return code %s (task deferral)", return_code)
            _set_task_deferred_context_var()
        else:
            self.log.info("Task exited with return code %s", return_code)
        if not self.task_instance.test_mode and not is_deferral:
            if conf.getboolean("scheduler", "schedule_after_task_execution", fallback=True):
                self.task_instance.schedule_downstream_tasks()
    def on_kill(self):
        self.task_runner.terminate()
        self.task_runner.on_finish()
    @provide_session
    def heartbeat_callback(self, session: Session = NEW_SESSION) -> None: