### 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)

"""
        Get the concurrency maps.
        :param states: List of states to query for
        :return: Concurrency map
        """
        ti_concurrency_query: Result = session.execute(
            select(TI.task_id, TI.run_id, TI.dag_id, func.count("*"))
            .where(TI.state.in_(states))
            .group_by(TI.task_id, TI.run_id, TI.dag_id)
        )
        return ConcurrencyMap.from_concurrency_map(
            {(dag_id, run_id, task_id): count for task_id, run_id, dag_id, count in ti_concurrency_query}
        )
    def _executable_task_instances_to_queued(self, max_tis: int, session: Session) -> list[TI]:
        """
        Find TIs that are ready for execution based on conditions.
        Conditions include:
        - pool limits
        - DAG max_active_tasks
        - executor state
        - priority
        - max active tis per DAG