### 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)

from airflow.dag_processing.manager import DagFileProcessorAgent
        self.log.info("Starting the scheduler")
        executor_class, _ = ExecutorLoader.import_default_executor_cls()
        
        pickle_dags = self.do_pickle and executor_class.supports_pickling
        self.log.info("Processing each file at most %s times", self.num_times_parse_dags)
        
        
        async_mode = not self.using_sqlite
        processor_timeout_seconds: int = conf.getint("core", "dag_file_processor_timeout")
        processor_timeout = timedelta(seconds=processor_timeout_seconds)
        if not self._standalone_dag_processor and not self.processor_agent:
            self.processor_agent = DagFileProcessorAgent(
                dag_directory=Path(self.subdir),
                max_runs=self.num_times_parse_dags,
                processor_timeout=processor_timeout,
                dag_ids=[],
                pickle_dags=pickle_dags,
                async_mode=async_mode,
            )
        try:
            self.job.executor.job_id = self.job.id
            if self.processor_agent:
                self.log.debug("Using PipeCallbackSink as callback sink.")
                self.job.executor.callback_sink = PipeCallbackSink(
                    get_sink_pipe=self.processor_agent.get_callbacks_pipe
                )
            else:
                from airflow.callbacks.database_callback_sink import DatabaseCallbackSink