**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
)
        for task_instance in tis_to_set_to_scheduled:
            self.executor.queued_tasks.pop(task_instance.key)
        task_instance_str = "\n\t".join(repr(x) for x in tis_to_set_to_scheduled)
        self.log.info("Set the following tasks to scheduled state:\n\t%s", task_instance_str)
    @provide_session
    def _process_executor_events(self, session: Session = None) -> int:
        """Respond to executor events."""
        if not self.processor_agent:
            raise ValueError("Processor agent is not started.")
        ti_primary_key_to_try_number_map: Dict[Tuple[str, str, datetime.datetime], int] = {}
        event_buffer = self.executor.get_event_buffer()
        tis_with_right_state: List[TaskInstanceKey] = []
        
        for ti_key, value in event_buffer.items():
            state: str
            state, _ = value
            
            ti_primary_key_to_try_number_map[ti_key.primary] = ti_key.try_number
            self.log.info(
                "Executor reports execution of %s.%s execution_date=%s "
                "exited with status %s for try_number %s",
                ti_key.dag_id,
                ti_key.task_id,
                ti_key.execution_date,
                state,
                ti_key.try_number,
            )
            if state in (State.FAILED, State.SUCCESS, State.QUEUED):
                tis_with_right_state.append(ti_key)