**Apache Airflow version**: 2.0.0


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

**Environment**:

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

**What happened**:

I have the following DAG:

```py
from datetime import datetime, timedelta

from airflow import DAG
from airflow.operators.bash_operator import BashOperator

with DAG(
    dag_id="sla_trigger",
    schedule_interval="* * * * *",
    start_date=datetime(2020, 2, 3),
) as dag:
    BashOperator(
        task_id="bash_task",
        bash_command="sleep 30",
        sla=timedelta(seconds=2),
    )

```

And in my understanding this dag should result in SLA miss every time it is triggered (every minute). However, after few minutes of running I don't see any SLA misses...

**What you expected to happen**:

I expect to see SLA if task takes longer than expected.

**How to reproduce it**:

Use the dag from above.

**Anything else we need to know**:

N/A

airflow/jobs/scheduler_job.py
"""
    Process a Python file containing Airflow DAGs.
    This includes:
    1. Execute the file and look for DAG objects in the namespace.
    2. Execute any Callbacks if passed to DagFileProcessor.process_file
    3. Serialize the DAGs and save it to DB (or update existing record in the DB).
    4. Pickle the DAG and save it to the DB (if necessary).
    5. Record any errors importing the file into ORM
    Returns a tuple of 'number of dags found' and 'the count of import errors'
    :param dag_ids: If specified, only look at these DAG ID's
    :type dag_ids: List[str]
    :param log: Logger to save the processing process
    :type log: logging.Logger
    """
    UNIT_TEST_MODE: bool = conf.getboolean('core', 'UNIT_TEST_MODE')
    def __init__(self, dag_ids: Optional[List[str]], log: logging.Logger):