**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
.filter(
                DagRun.dag_id.in_([o.dag_id for o in dag_models]),
                DagRun.state == State.RUNNING,  
                DagRun.external_trigger.is_(False),
            )
            .group_by(DagRun.dag_id)
            .all()
        )
        for dag_model in dag_models:
            
            
            try:
                dag = self.dagbag.get_dag(dag_model.dag_id, session=session)
            except SerializedDagNotFound:
                self.log.exception("DAG '%s' not found in serialized_dag table", dag_model.dag_id)
                continue
            active_runs_of_dag = active_runs_of_dags.get(dag.dag_id, 0)
            if dag.max_active_runs and active_runs_of_dag >= dag.max_active_runs:
                self.log.info(
                    "DAG %s is at (or above) max_active_runs (%d of %d), not creating any more runs",
                    dag.dag_id,
                    active_runs_of_dag,
                    dag.max_active_runs,
                )
                dag_model.next_dagrun_create_after = None
            else:
                dag_model.next_dagrun, dag_model.next_dagrun_create_after = dag.next_dagrun_info(
                    dag_model.next_dagrun
                )
    def _schedule_dag_run(