**Apache Airflow version**: 2.0.1
**Kubernetes version**: v1.19.7
**Executor**: KubernetesExecutor

**What happened**:

If you have a worker that gets stuck in pending forever, say with a missing volume mount, the task will stay in the queued state forever. Nothing is applying a timeout on it actually being able to start.

**What you expected to happen**:

Eventually the scheduler will notice that the worker hasn't progressed past pending after a given amount of time and will mark it as a failure.

**How to reproduce it**:
```python
from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.utils.dates import days_ago
from kubernetes.client import models as k8s


default_args = {
    "owner": "airflow",
}

with DAG(
    dag_id="pending",
    default_args=default_args,
    schedule_interval=None,
    start_date=days_ago(2),
) as dag:
    BashOperator(
        task_id="forever_pending",
        bash_command="date; sleep 30; date",
        executor_config={
            "pod_override": k8s.V1Pod(
                spec=k8s.V1PodSpec(
                    containers=[
                        k8s.V1Container(
                            name="base",
                            volume_mounts=[
                                k8s.V1VolumeMount(mount_path="/foo/", name="vol")
                            ],
                        )
                    ],
                    volumes=[
                        k8s.V1Volume(
                            name="vol",
                            persistent_volume_claim=k8s.V1PersistentVolumeClaimVolumeSource(
                                claim_name="missing"
                            ),
                        )
                    ],
                )
            ),
        },
    )
```


**Anything else we need to know**:
Related to:

* #15149 (This is reporting that these pending pods don't get deleted via "Mark Failed")
* #14556 (This handles when these pending pods get deleted and is already fixed)

airflow/jobs/scheduler_job.py
"""Launch the process and start processing the DAG."""
        start_method = self._get_multiprocessing_start_method()
        context = multiprocessing.get_context(start_method)
        _parent_channel, _child_channel = context.Pipe(duplex=False)
        process = context.Process(
            target=type(self)._run_file_processor,
            args=(
                _child_channel,
                _parent_channel,
                self.file_path,
                self._pickle_dags,
                self._dag_ids,
                f"DagFileProcessor{self._instance_id}",
                self._callback_requests,
            ),
            name=f"DagFileProcessor{self._instance_id}-Process",
        )
        self._process = process
        self._start_time = timezone.utcnow()
        process.start()
        
        
        _child_channel.close()
        del _child_channel
        
        
        self._parent_channel = _parent_channel
    def kill(self) -> None: