### 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)

self.log.warning(
                    "Recorded pid %s does not match the current pid %s", recorded_pid, current_pid
                )
                raise AirflowException("PID of job runner does not match")
        elif self.task_runner.return_code() is None and hasattr(self.task_runner, "process"):
            if ti.state == State.SKIPPED:
                
                dagrun = ti.get_dagrun(session=session)
                execution_time = (dagrun.end_date or timezone.utcnow()) - dagrun.start_date
                if ti.task.dag is not None:
                    dagrun_timeout = ti.task.dag.dagrun_timeout
                else:
                    dagrun_timeout = None
                if dagrun_timeout and execution_time > dagrun_timeout:
                    self.log.warning("DagRun timed out after %s.", str(execution_time))
            
            
            
            if self._state_change_checks >= 1:  
                self.log.warning(
                    "State of this instance has been externally set to %s. Terminating instance.", ti.state
                )
                self.terminating = True
            self._state_change_checks += 1
    def _log_return_code_metric(self, return_code: int):
        Stats.incr(
            "local_task_job.task_exit."
            f"{self.job.id}.{self.task_instance.dag_id}.{self.task_instance.task_id}.{return_code}"
        )