### 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)

"""
        Harvest DAG parsing results, queue tasks, and perform executor heartbeat; the actual scheduler loop.
        The main steps in the loop are:
            #. Harvest DAG parsing results through DagFileProcessorAgent
            #. Find and queue executable tasks
                #. Change task instance state in DB
                #. Queue tasks in executor
            #. Heartbeat executor
                #. Execute queued tasks in executor asynchronously
                #. Sync on the states of running tasks
        Following is a graphic representation of these steps.
        .. image:: ../docs/apache-airflow/img/scheduler_loop.jpg
        """
        if not self.processor_agent and not self._standalone_dag_processor:
            raise ValueError("Processor agent is not started.")
        is_unit_test: bool = conf.getboolean("core", "unit_test_mode")