**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
from airflow.exceptions import AirflowException, SerializedDagNotFound, TaskNotFound
from airflow.executors.executor_loader import UNPICKLEABLE_EXECUTORS
from airflow.jobs.base_job import BaseJob
from airflow.models import DAG, DagModel, SlaMiss, errors
from airflow.models.dagbag import DagBag
from airflow.models.dagrun import DagRun
from airflow.models.serialized_dag import SerializedDagModel
from airflow.models.taskinstance import SimpleTaskInstance, TaskInstanceKey
from airflow.settings import run_with_db_retries
from airflow.stats import Stats
from airflow.ti_deps.dependencies_states import EXECUTION_STATES
from airflow.utils import timezone
from airflow.utils.callback_requests import (
    CallbackRequest,
    DagCallbackRequest,
    SlaCallbackRequest,
    TaskCallbackRequest,
)
from airflow.utils.dag_processing import AbstractDagFileProcessorProcess, DagFileProcessorAgent
from airflow.utils.email import get_email_address_list, send_email
from airflow.utils.log.logging_mixin import LoggingMixin, StreamLogWriter, set_context
from airflow.utils.mixins import MultiprocessingStartMethodMixin
from airflow.utils.session import create_session, provide_session
from airflow.utils.sqlalchemy import is_lock_not_available_error, prohibit_commit, skip_locked, with_row_locks
from airflow.utils.state import State
from airflow.utils.types import DagRunType
TI = models.TaskInstance
DR = models.DagRun
DM = models.DagModel
class DagFileProcessorProcess(AbstractDagFileProcessorProcess, LoggingMixin, MultiprocessingStartMethodMixin):