**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
:rtype: Tuple[int, int]
        """
        self.log.info("Processing file %s for tasks to queue", file_path)
        try:
            dagbag = DagBag(file_path, include_examples=False, include_smart_sensor=False)
        except Exception:  
            self.log.exception("Failed at reloading the DAG file %s", file_path)
            Stats.incr('dag_file_refresh_error', 1, 1)
            return 0, 0
        if len(dagbag.dags) > 0:
            self.log.info("DAG(s) %s retrieved from %s", dagbag.dags.keys(), file_path)
        else:
            self.log.warning("No viable dags retrieved from %s", file_path)
            self.update_import_errors(session, dagbag)
            return 0, len(dagbag.import_errors)
        self.execute_callbacks(dagbag, callback_requests)
        
        dagbag.sync_to_db()
        if pickle_dags:
            paused_dag_ids = DagModel.get_paused_dag_ids(dag_ids=dagbag.dag_ids)
            unpaused_dags: List[DAG] = [
                dag for dag_id, dag in dagbag.dags.items() if dag_id not in paused_dag_ids
            ]
            for dag in unpaused_dags:
                dag.pickle(session)
        
        try:
            self.update_import_errors(session, dagbag)
        except Exception:  
            self.log.exception("Error logging import errors!")