[question] current_rewards in evaluations.py

### 🐛 Bug

I have a discrepancy with how evaluate.py is structured.

I am trying to evaluate DQN on Atari Environments (specifically: Breakout). I have defined my environment like this:

```python
# === Setup Environment ===

# Training environment
env = make_atari_env(args.env_name, n_envs=args.n_envs, seed=args.seed)
env = VecFrameStack(env, n_stack=args.frame_stack)
env = VecTransposeImage(env)

# Evaluation environment
eval_env = make_atari_env(args.env_name, n_envs=args.n_envs, seed=args.seed+1000)
eval_env = VecFrameStack(eval_env, n_stack=args.frame_stack)
eval_env = VecTransposeImage(eval_env)

# Set seeds for reproducibility
np.random.seed(args.seed)
random.seed(args.seed)
torch.manual_seed(args.seed)
```

Now in the evaluation.py script located at `stable_baselines3/common/evaluation.py` whenever a `done` is emitted, there are two logical paths the evaluate function can follow

1. Checking if the environment is montior wrapped or not
2. If not, it assumes that `done` being True means that the episode is over and appends `current_rewards` and `current_lengths` to `episode_rewards` and `episode_lengths` respectively and resets them.

My issue is:
When an environment is Montior wrapped, shouldn't `current_rewards` and `current_lengths` be reset to zero if and only if `"episode" in info.keys()`? Else, you're just tracking the rewards and lengths for one life in the Atari game.

So my argument is essentially this in one line:

When an environment is Monitor wrapped, instead of pre-maturely resetting current_rewards and current_lengths to be zero based on the `done`, I think it's prudent to set it back to zero when the episode actually ends.

I realize that current_rewards and current_lengths aren't really important in Atari (or more generally, Monitor Wrapped environments) environments because ultimately the episode_rewards and episode_lengths come from `info["episode"]["r"]` however, this is something that should be looked into and I'm happy to open a PR with proper comments and explanations.

### To Reproduce

```python
import warnings
from typing import Any, Callable, Optional, Union

import gymnasium as gym
import numpy as np

from stable_baselines3.common import type_aliases
from stable_baselines3.common.vec_env import DummyVecEnv, VecEnv, VecMonitor, is_vecenv_wrapped

def evaluate_policy(
model: "type_aliases.PolicyPredictor",
env: Union[gym.Env, VecEnv],
n_eval_episodes: int = 10,
deterministic: bool = True,
render: bool = False,
callback: Optional[Callable[[dict[str, Any], dict[str, Any]], None]] = None,
reward_threshold: Optional[float] = None,
return_episode_rewards: bool = False,
warn: bool = True,
) -> Union[tuple[float, float], tuple[list[float], list[int]]]:

......
......
......

if dones[i]:
if is_monitor_wrapped:
# Atari wrapper can send a "done" signal when
# the agent loses a life, but it does not correspond
# to the true end of episode
if "episode" in info.keys():
# Do not trust "done" with episode endings.
# Monitor wrapper includes "episode" key in info if environment
# has been wrapped with it. Use those rewards instead.
episode_rewards.append(info["episode"]["r"])
episode_lengths.append(info["episode"]["l"])
# Only increment at the real end of an episode
episode_counts[i] += 1
else:
episode_rewards.append(current_rewards[i])
episode_lengths.append(current_lengths[i])
episode_counts[i] += 1

current_rewards[i] = 0
current_lengths[i] = 0

observations = new_observations
```

### Relevant log output / Error message

```shell

```

### System Info

```
- OS: Linux-6.8.0-59-generic-x86_64-with-glibc2.35 # 61~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 15 17:03:15 UTC 2
- Python: 3.10.12
- Stable-Baselines3: 2.6.0
- PyTorch: 2.7.0+cu126
- GPU Enabled: True
- Numpy: 2.2.6
- Cloudpickle: 3.1.1
- Gymnasium: 1.1.1
```

### Checklist

- [x] My issue does not relate to a custom gym environment. (Use the custom gym env template instead)
- [x] I have checked that there is no similar [issue](https://github.com/DLR-RM/stable-baselines3/issues) in the repo
- [x] I have read the [documentation](https://stable-baselines3.readthedocs.io/en/master/)
- [x] I have provided a [minimal and working](https://github.com/DLR-RM/stable-baselines3/issues/982#issuecomment-1197044014) example to reproduce the bug
- [x] I've used the [markdown code blocks](https://help.github.com/en/articles/creating-and-highlighting-code-blocks) for both code and stack traces.