### What do you see as an issue?

The documentation for zombie tasks https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/tasks.html#zombie-undead-tasks is a bit abstract at the moment. There is a scope for enhancing the document by explaining how Airflow detects tasks as zombies

### Solving the problem

We can enhance the documentation by translating this query https://github.com/astronomer/airflow/blob/main/airflow/jobs/scheduler_job_runner.py#L1721 to a layman readable text in our documentation.

### Anything else

It might also help to add developer contribution steps to reproduce zombies locally.

### Are you willing to submit 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)

task_instance,
                        task_instance.pool_slots,
                        pool_total,
                        pool_name,
                    )
                    pool_num_starving_tasks[pool_name] += 1
                    num_starving_tasks_total += 1
                    starved_tasks.add((task_instance.dag_id, task_instance.task_id))
                    continue
                if task_instance.pool_slots > open_slots:
                    self.log.info(
                        "Not executing %s since it requires %s slots "
                        "but there are %s open slots in the pool %s.",
                        task_instance,
                        task_instance.pool_slots,
                        open_slots,
                        pool_name,
                    )
                    pool_num_starving_tasks[pool_name] += 1
                    num_starving_tasks_total += 1
                    starved_tasks.add((task_instance.dag_id, task_instance.task_id))
                    
                    continue
                
                
                dag_id = task_instance.dag_id
                current_active_tasks_per_dag = concurrency_map.dag_active_tasks_map[dag_id]
                max_active_tasks_per_dag_limit = task_instance.dag_model.max_active_tasks
                self.log.info(
                    "DAG %s has %s/%s running and queued tasks",