run `DiceMetric.aggregate()` more than once produce inaccurate results

[yiyixuxu]

Describe the bug
when you apply aggregate() a DiceMetric object more than once, it will treat nan as 0 in the calculation, resulting in a different and inaccurate mean

To Reproduce
https://gist.github.com/yiyixuxu/861d731d682fbb30fa281990f5e1980f

from monai.metrics import DiceMetric
from monai.losses.dice import *  # NOQA
import torch
input1 = torch.tensor(
    [[[[0, 0],
       [0, 0]], 
      [[1, 1],
       [0, 1]],
     [[0, 0],
      [1, 0]]]])
target1 = torch.tensor(
    [[[[1., 0.],
       [1., 1.]],
      
      [[0., 1.],
       [0., 0.]],
     
     [[0., 0.],
      [0., 0.]]]])
dice_metric = DiceMetric(include_background=True, reduction="mean")
dice1 = dice_metric(y_pred=input1, y=target1)
print(f'dice1: {dice1}')
>>> dice1: tensor([[0.0000, 0.5000,    nan]])
print(f'dice_metric.aggregate().item():{dice_metric.aggregate().item()}')
>>>dice_metric.aggregate().item():0.25
print(f'dice_metric.aggregate().item():{dice_metric.aggregate().item()}')
>>>dice_metric.aggregate().item():0.1666666716337204
``
**Expected behavior**
It should have consistent results as `0.25` 


**Additional context**
this happens because the `do_metric_reduction` method modify the cache of buffer data in place;  
https://github.com/Project-MONAI/MONAI/blob/dev/monai/metrics/utils.py#L45