**Apache Airflow version**: 2.0.0rc3

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):

**Environment**:

- **Cloud provider or hardware configuration**: Linux
- **OS** (e.g. from /etc/os-release): Ubuntu
- **Kernel** (e.g. `uname -a`): 
- **Install tools**:
- **Others**:

**What happened**:

After shutting down the scheduler, while tasks were in running state, trying to restart the scheduler results in pk violations..

```[2020-12-15 22:43:29,673] {scheduler_job.py:1293} ERROR - Exception when executing SchedulerJob._run_scheduler_loop
Traceback (most recent call last):
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1277, in _execute_context
    cursor, statement, parameters, context
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 593, in do_execute
    cursor.execute(statement, parameters)
psycopg2.errors.UniqueViolation: duplicate key value violates unique constraint "dag_run_dag_id_run_id_key"
DETAIL:  Key (dag_id, run_id)=(example_task_group, scheduled__2020-12-14T04:31:00+00:00) already exists.
```


**What you expected to happen**:

Scheduler restarts and picks up where it left off.

**How to reproduce it**:

Set example dag ( I used task_group) to schedule_interval `* * * * *` and start the scheduler and let it run for a few minutes. 
Shut down the scheduler
Attempt to restart the scheduler

**Anything else we need to know**:
I came across this doing testing using the LocalExecutor in a virtual env. If no else is able to reproduce it, I'll try again in a clean virtual env.

airflow/models/serialized_dag.py
"""A table for serialized DAGs.
    serialized_dag table is a snapshot of DAG files synchronized by scheduler.
    This feature is controlled by:
    * ``[core] min_serialized_dag_update_interval = 30`` (s):
      serialized DAGs are updated in DB when a file gets processed by scheduler,
      to reduce DB write rate, there is a minimal interval of updating serialized DAGs.
    * ``[scheduler] dag_dir_list_interval = 300`` (s):
      interval of deleting serialized DAGs in DB when the files are deleted, suggest
      to use a smaller interval such as 60
    It is used by webserver to load dags
    because reading from database is lightweight compared to importing from files,
    it solves the webserver scalability issue.
    """
    __tablename__ = 'serialized_dag'
    dag_id = Column(String(ID_LEN), primary_key=True)
    fileloc = Column(String(2000), nullable=False)
    
    fileloc_hash = Column(BigInteger, nullable=False)
    data = Column(sqlalchemy_jsonfield.JSONField(json=json), nullable=False)