### 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)

else:
            if self.state == State.QUEUED:
                
                self._try_number += 1
            self.state = State.UP_FOR_RETRY
            email_for_state = operator.attrgetter("email_on_retry")
            callbacks = task.on_retry_callback if task else None
            callback_type = "on_retry"
        self._log_state("Immediate failure requested. " if force_fail else "")
        if task and email_for_state(task) and task.email:
            try:
                self.email_alert(error, task)
            except Exception:
                self.log.exception("Failed to send email to: %s", task.email)
        if callbacks and context:
            self._run_finished_callback(callbacks, context, callback_type)
        if not test_mode:
            session.merge(self)
            session.flush()
    def is_eligible_to_retry(self):
        """Is task instance is eligible for retry"""
        if self.state == State.RESTARTING:
            
            
            return True
        if not getattr(self, "task", None):
            
            return self.try_number <= self.max_tries
        return self.task.retries and self.try_number <= self.max_tries
    def get_template_context(