### 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)

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
                    )
                    
                    pool_num_starving_tasks[pool_name] += 1