### 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)

...
    @provide_session
    def _emit_pool_metrics(self, session: Session = NEW_SESSION) -> None:
        from airflow.models.pool import Pool
        pools = Pool.slots_stats(session=session)
        for pool_name, slot_stats in pools.items():
            Stats.gauge(f"pool.open_slots.{pool_name}", slot_stats["open"])
            Stats.gauge(f"pool.queued_slots.{pool_name}", slot_stats["queued"])
            Stats.gauge(f"pool.running_slots.{pool_name}", slot_stats["running"])
            
            Stats.gauge("pool.open_slots", slot_stats["open"], tags={"pool_name": pool_name})
            Stats.gauge("pool.queued_slots", slot_stats["queued"], tags={"pool_name": pool_name})
            Stats.gauge("pool.running_slots", slot_stats["running"], tags={"pool_name": pool_name})
    @provide_session
    def adopt_or_reset_orphaned_tasks(self, session: Session = NEW_SESSION) -> int:
        """
        Reset any TaskInstance still in QUEUED or SCHEDULED states that were
        enqueued by a SchedulerJob that is no longer running.
        :return: the number of TIs reset
        """
        self.log.info("Resetting orphaned tasks for active dag runs")
        timeout = conf.getint("scheduler", "scheduler_health_check_threshold")
        for attempt in run_with_db_retries(logger=self.log):
            with attempt:
                self.log.debug(
                    "Running SchedulerJob.adopt_or_reset_orphaned_tasks with retries. Try %d of %d",
                    attempt.retry_state.attempt_number,
                    MAX_DB_RETRIES,
                )
                self.log.debug("Calling SchedulerJob.adopt_or_reset_orphaned_tasks method")