### 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)

).all()
                timer.stop(send=True)
            except OperationalError as e:
                timer.stop(send=False)
                raise e
            
            
            if len(task_instances_to_examine) == 0:
                self.log.debug("No tasks to consider for execution.")
                break
            
            task_instance_str = "\n\t".join(repr(x) for x in task_instances_to_examine)
            self.log.info(
                "%s tasks up for execution:\n\t%s", len(task_instances_to_examine), task_instance_str
            )
            for task_instance in task_instances_to_examine:
                pool_name = task_instance.pool
                pool_stats = pools.get(pool_name)
                if not pool_stats:
                    self.log.warning("Tasks using non-existent pool '%s' will not be scheduled", pool_name)
                    starved_pools.add(pool_name)
                    continue
                
                pool_num_starving_tasks.setdefault(pool_name, 0)
                pool_total = pool_stats["total"]
                open_slots = pool_stats["open"]
                if open_slots <= 0:
                    self.log.info(
                        "Not scheduling since there are %s open slots in pool %s", open_slots, pool_name
                    )