### Description

A count of datasets that are currently registered/declared in an Airflow deployment.

### Use case/motivation

Would be nice to see how deployments are adopting datasets.

### Related issues

_No response_

### 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)

pools = Pool.slots_stats(lock_rows=True, session=session)
        
        
        pool_slots_free = sum(max(0, pool["open"]) for pool in pools.values())
        if pool_slots_free == 0:
            self.log.debug("All pools are full!")
            return []
        max_tis = min(max_tis, pool_slots_free)
        starved_pools = {pool_name for pool_name, stats in pools.items() if stats["open"] <= 0}
        
        dag_active_tasks_map: DefaultDict[str, int]
        task_concurrency_map: DefaultDict[tuple[str, str], int]
        dag_active_tasks_map, task_concurrency_map = self.__get_concurrency_maps(
            states=list(EXECUTION_STATES), session=session
        )
        num_tasks_in_executor = 0
        
        num_starving_tasks_total = 0
        
        starved_dags: set[str] = set()
        starved_tasks: set[tuple[str, str]] = set()
        pool_num_starving_tasks: DefaultDict[str, int] = defaultdict(int)
        for loop_count in itertools.count(start=1):
            num_starved_pools = len(starved_pools)
            num_starved_dags = len(starved_dags)
            num_starved_tasks = len(starved_tasks)