**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
):
                self.log.info(
                    "DAG %s already has %d active runs, not queuing any tasks for run %s",
                    dag.dag_id,
                    len(currently_active_runs),
                    dag_run.execution_date,
                )
                return 0
        self._verify_integrity_if_dag_changed(dag_run=dag_run, session=session)
        
        schedulable_tis, callback_to_run = dag_run.update_state(session=session, execute_callbacks=False)
        self._send_dag_callbacks_to_processor(dag_run, callback_to_run)
        
        
        
        
        return dag_run.schedule_tis(schedulable_tis, session)
    @provide_session
    def _verify_integrity_if_dag_changed(self, dag_run: DagRun, session=None):
        """Only run DagRun.verify integrity if Serialized DAG has changed since it is slow"""
        latest_version = SerializedDagModel.get_latest_version_hash(dag_run.dag_id, session=session)
        if dag_run.dag_hash == latest_version:
            self.log.debug("DAG %s not changed structure, skipping dagrun.verify_integrity", dag_run.dag_id)
            return
        dag_run.dag_hash = latest_version
        
        dag_run.dag = self.dagbag.get_dag(dag_id=dag_run.dag_id, session=session)
        
        dag_run.verify_integrity(session=session)
    def _send_dag_callbacks_to_processor(