### Apache Airflow version

2.2.0 (latest released)

### Operating System

Container-Optimized OS with Containerd (cos_containerd) - GKE

### Versions of Apache Airflow Providers

_No response_

### Deployment

Astronomer

### Deployment details

_No response_

### What happened

I went to clear a successful task instance that is using a Deferred operator, and noticed it immediately went into a state of successful. The Operator is designed to run a trigger that will wait 1 minute from approximately task start. Upon investigation, I did not see the following in the second run's task log: `{taskinstance.py:1332} INFO - Pausing task as DEFERRED.`.

### What you expected to happen

I would expect the task to once again go into a state of deferred for approx. 1 minute and then succeed.

### How to reproduce

Run the following DAG. Once a task instance is successful, clear it, and see the task fail. This confirms `ti.next_method` was not cleared, as `execute` did not rerun and reset `execute_try_number`. `execute` did not rerun, as `ti.next_method` was not cleared.

```
from datetime import datetime

from airflow import DAG
from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.triggers.testing import SuccessTrigger


class RetryOperator(BaseOperator):
    def execute(self, context):
        ti = context["ti"]
        next_method = ti.next_method
        try_number = ti.try_number

        self.log.info(
            f"In `execute`: try_number: {try_number}, next_method {next_method}."
        )

        self.defer(
            trigger=SuccessTrigger(),
            method_name="next",
            kwargs={"execute_try_number": try_number},
        )

    def next(self, context, execute_try_number, event=None):
        ti = context["ti"]
        next_method = ti.next_method
        try_number = ti.try_number

        self.log.info(
            f"In `next`: try_number: {try_number}, next_method {next_method}, execute_try_number: {execute_try_number}."
        )

        if execute_try_number != try_number:
            raise AirflowException("`execute` wasn't run during clear!")

        return None  # Success!


with DAG(
    "triggerer_clear", schedule_interval=None, start_date=datetime(2021, 10, 20)
) as dag:
    RetryOperator(task_id="clear")
```

### Anything else

I believe this is due to `ti.next_method` (and `ti.next_method_kwargs`) not being cleared after a task has completed. A similar issue was raised in https://github.com/apache/airflow/issues/18146.

### Are you willing to submit PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

airflow/models/taskinstance.py
"""For API-compatibly with TaskInstance.
        Returns self
        """
        return self
class TaskInstance(Base, LoggingMixin):
    """
    Task instances store the state of a task instance. This table is the
    authority and single source of truth around what tasks have run and the
    state they are in.
    The SqlAlchemy model doesn't have a SqlAlchemy foreign key to the task or
    dag model deliberately to have more control over transactions.
    Database transactions on this table should insure double triggers and
    any confusion around what task instances are or aren't ready to run
    even while multiple schedulers may be firing task instances.
    """
    __tablename__ = "task_instance"
    task_id = Column(String(ID_LEN, **COLLATION_ARGS), primary_key=True, nullable=False)
    dag_id = Column(String(ID_LEN, **COLLATION_ARGS), primary_key=True, nullable=False)
    run_id = Column(String(ID_LEN, **COLLATION_ARGS), primary_key=True, nullable=False)
    start_date = Column(UtcDateTime)
    end_date = Column(UtcDateTime)
    duration = Column(Float)
    state = Column(String(20))
    _try_number = Column('try_number', Integer, default=0)
    max_tries = Column(Integer)
    hostname = Column(String(1000))
    unixname = Column(String(1000))