**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
with Stats.timer() as timer:
                if self.using_sqlite:
                    self.processor_agent.run_single_parsing_loop()
                    
                    
                    self.log.debug("Waiting for processors to finish since we're using sqlite")
                    self.processor_agent.wait_until_finished()
                with create_session() as session:
                    num_queued_tis = self._do_scheduling(session)
                    self.executor.heartbeat()
                    session.expunge_all()
                    num_finished_events = self._process_executor_events(session=session)
                self.processor_agent.heartbeat()
                
                self.heartbeat(only_if_necessary=True)
                
                next_event = timers.run(blocking=False)
                self.log.debug("Next timed event is in %f", next_event)
            self.log.debug("Ran scheduling loop in %.2f seconds", timer.duration)
            if not is_unit_test and not num_queued_tis and not num_finished_events:
                
                
                
                time.sleep(min(self._processor_poll_interval, next_event))
            if loop_count >= self.num_runs > 0:
                self.log.info(
                    "Exiting scheduler loop as requested number of runs (%d - got to %d) has been reached",
                    self.num_runs,
                    loop_count,
                )