**Apache Airflow version**: 2.0.0a1

**What happened**:

On my dev environment I have a lot of DAG runs with failed tasks, where the *downstream* task instances then have been set to `upstream_failed`. The scheduler is now refusing to schedule more instances for such DAGs.

The scheduler looks for such task instances that are not in the `State.finished` set:

https://github.com/apache/airflow/blob/db3fe0926bb75008311eed804052c90bfa912424/airflow/jobs/scheduler_job.py#L1482-L1488

However, `State.finished` is currently not a proper inverse of *running* states, it only includes success, failed, and skipped:

https://github.com/apache/airflow/blob/db3fe0926bb75008311eed804052c90bfa912424/airflow/utils/state.py#L110-L114

What is missing is the task instance `upstream_failed` state, so currently my log is being filled (very rapidly) with:

```
[2020-10-18 13:22:18,503] {{scheduler_job.py:1658}} INFO - DAG test_dag already has 26 active runs, not queuing any more tasks
```

Those 26 'active runs' are simply dag runs that failed and have 'upstream_failed' task instances.

Either `upstream_failed` should be added to `State.finished`, or the Scheduler task instance test should explicitly take that state into account.
airflow/utils/state.py
UP_FOR_RETRY,
        UP_FOR_RESCHEDULE,
        QUEUED,
        NONE,
        SCHEDULED,
        SENSING,
    )
    dag_states = (
        SUCCESS,
        RUNNING,
        FAILED,
    )
    state_color = {
        QUEUED: 'gray',
        RUNNING: 'lime',
        SUCCESS: 'green',
        SHUTDOWN: 'blue',
        FAILED: 'red',
        UP_FOR_RETRY: 'gold',
        UP_FOR_RESCHEDULE: 'turquoise',
        UPSTREAM_FAILED: 'orange',
        SKIPPED: 'pink',
        REMOVED: 'lightgrey',
        SCHEDULED: 'tan',
        NONE: 'lightblue',
        SENSING: 'lightseagreen',
    }
    state_color.update(STATE_COLORS)  
    @classmethod
    def color(cls, state):