**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.
tests/ti_deps/deps/test_trigger_rule_dep.py
#
#
#
import unittest
from datetime import datetime
from unittest.mock import Mock
from airflow import settings
from airflow.models import DAG, TaskInstance
from airflow.models.baseoperator import BaseOperator
from airflow.operators.dummy_operator import DummyOperator
from airflow.ti_deps.deps.trigger_rule_dep import TriggerRuleDep
from airflow.utils import timezone
from airflow.utils.session import create_session
from airflow.utils.state import State
from airflow.utils.trigger_rule import TriggerRule
from tests.models import DEFAULT_DATE
from tests.test_utils.db import clear_db_runs
class TestTriggerRuleDep(unittest.TestCase):
    def _get_task_instance(self, trigger_rule=TriggerRule.ALL_SUCCESS,
                           state=None, upstream_task_ids=None):
        task = BaseOperator(task_id='test_task', trigger_rule=trigger_rule,
                            start_date=datetime(2015, 1, 1))
        if upstream_task_ids:
            task._upstream_task_ids.update(upstream_task_ids)
        return TaskInstance(task=task, state=state, execution_date=task.start_date)
    def test_no_upstream_tasks(self):