**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
if not tis_with_right_state:
            return len(event_buffer)
        
        filter_for_tis = TI.filter_for_tis(tis_with_right_state)
        tis: List[TI] = session.query(TI).filter(filter_for_tis).options(selectinload('dag_model')).all()
        for ti in tis:
            try_number = ti_primary_key_to_try_number_map[ti.key.primary]
            buffer_key = ti.key.with_try_number(try_number)
            state, info = event_buffer.pop(buffer_key)
            
            if state == State.QUEUED:
                ti.external_executor_id = info
                self.log.info("Setting external_id for %s to %s", ti, info)
                continue
            if ti.try_number == buffer_key.try_number and ti.state == State.QUEUED:
                Stats.incr('scheduler.tasks.killed_externally')
                msg = (
                    "Executor reports task instance %s finished (%s) although the "
                    "task says its %s. (Info: %s) Was the task killed externally?"
                )
                self.log.error(msg, ti, state, ti.state, info)
                request = TaskCallbackRequest(
                    full_filepath=ti.dag_model.fileloc,
                    simple_task_instance=SimpleTaskInstance(ti),
                    msg=msg % (ti, state, ti.state, info),
                )
                self.processor_agent.send_callback_to_execute(request)
        return len(event_buffer)
    def _execute(self) -> None: