### 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)

"""Get failed Dependencies"""
        dep_context = dep_context or DepContext()
        for dep in dep_context.deps | self.task.deps:
            for dep_status in dep.get_dep_statuses(self, session, dep_context):
                self.log.debug(
                    "%s dependency '%s' PASSED: %s, %s",
                    self,
                    dep_status.dep_name,
                    dep_status.passed,
                    dep_status.reason,
                )
                if not dep_status.passed:
                    yield dep_status
    def __repr__(self) -> str:
        prefix = f"<TaskInstance: {self.dag_id}.{self.task_id} {self.run_id} "
        if self.map_index != -1:
            prefix += f"map_index={self.map_index} "
        return prefix + f"[{self.state}]>"
    def next_retry_datetime(self):