### 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)

"The '[celery] task_adoption_timeout' config option is deprecated. "
                "Please update your config to use '[scheduler] task_queued_timeout' instead.",
                DeprecationWarning,
            )
        worker_pods_pending_timeout = conf.getfloat(
            "kubernetes_executor", "worker_pods_pending_timeout", fallback=0
        )
        if worker_pods_pending_timeout:
            
            warnings.warn(
                "The '[kubernetes_executor] worker_pods_pending_timeout' config option is deprecated. "
                "Please update your config to use '[scheduler] task_queued_timeout' instead.",
                DeprecationWarning,
            )
        task_queued_timeout = conf.getfloat("scheduler", "task_queued_timeout")
        self._task_queued_timeout = max(
            stalled_task_timeout, task_adoption_timeout, worker_pods_pending_timeout, task_queued_timeout
        )
        self.do_pickle = do_pickle
        if log:
            self._log = log
        
        sql_conn: str = conf.get_mandatory_value("database", "sql_alchemy_conn").lower()
        self.using_sqlite = sql_conn.startswith("sqlite")
        
        self.processor_agent: DagFileProcessorAgent | None = None
        self.dagbag = DagBag(dag_folder=self.subdir, read_dags_from_db=True, load_op_links=False)
        self._paused_dag_without_running_dagruns: set = set()
    @provide_session
    def heartbeat_callback(self, session: Session = NEW_SESSION) -> None: