id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
1114351614,I_kwDOAMm_X85Ca6f-,6191,[Bug]: reading NaT/NaN on M1 ARM chip,387624,closed,0,,,8,2022-01-25T20:52:39Z,2023-09-17T08:15:28Z,2023-09-17T08:15:28Z,NONE,,,,"### What happened?

I have nan values in a date vector stored in a netCDF. When I read on my ARM Apple computer with `xr.open_dataset()`, it is not properly recognized.

For example, the following data is stored in a NetCDF:
```
date = pd.date_range(...)
date[4] = nan
```

Then when I read the file:
`date[4]` is set to `date[0]`, which is the first date of the range instead of a 'NaT'.

I understand that this issue is quite weird and it doesn't seem to happen on other OS. Actually, I try on MacOS (with an intel processor) and on two different Linux computers, and in those configurations, `date[4]` is properly set to 'NaT' after opening the netCDF with `xr.open_dataset()`. Note that I tried with the same version of xarray as well as with different versions, and I just can't seem to reproduce this issue on any machine except on the M1 ARM chip.

### What did you expect to happen?

I expect the following result after running the minimal example:

```
array(['2022-01-01T00:00:00.000000000', '2022-01-02T00:00:00.000000000',
       '2022-01-03T00:00:00.000000000', '2022-01-04T00:00:00.000000000',
                                 'NaT', '2022-01-06T00:00:00.000000000',
       '2022-01-07T00:00:00.000000000', '2022-01-08T00:00:00.000000000',
       '2022-01-09T00:00:00.000000000', '2022-01-10T00:00:00.000000000'],
      dtype='datetime64[ns]')
```

### Minimal Complete Verifiable Example

```python
import xarray as xr
import pandas as pd
import numpy as np

time = pd.date_range(start=""2022-01-01"",end=""2022-01-10"").to_pydatetime()
time[4] = np.datetime64(""NaT"")

ds = xr.Dataset(
    data_vars=dict(
        time=([""nt""], time),
    ),
)
ds.to_netcdf('test.nc')

ds_r = xr.open_dataset('test.nc')
ds_r.time
```


### Relevant log output

```python
array(['2022-01-01T00:00:00.000000000', '2022-01-02T00:00:00.000000000',
       '2022-01-03T00:00:00.000000000', '2022-01-04T00:00:00.000000000',
       '2022-01-01T00:00:00.000000000', '2022-01-06T00:00:00.000000000',
       '2022-01-07T00:00:00.000000000', '2022-01-08T00:00:00.000000000',
       '2022-01-09T00:00:00.000000000', '2022-01-10T00:00:00.000000000'],
      dtype='datetime64[ns]')
```


### Anything else we need to know?

_No response_

### Environment

INSTALLED VERSIONS
------------------
commit: None
python: 3.10.1 | packaged by conda-forge | (main, Dec 22 2021, 01:38:36) [Clang 11.1.0 ]
python-bits: 64
OS: Darwin
OS-release: 21.2.0
machine: arm64
processor: arm
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.12.1
libnetcdf: 4.8.1

xarray: 0.20.2
pandas: 1.3.5
numpy: 1.21.5
scipy: 1.7.3
netCDF4: 1.5.8
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.5.1.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2021.12.0
distributed: 2021.12.0
matplotlib: 3.5.1
cartopy: 0.20.1
seaborn: None
numbagg: None
fsspec: 2021.11.1
cupy: None
pint: None
sparse: None
setuptools: 60.0.4
pip: 21.3.1
conda: None
pytest: None
IPython: 8.0.0
sphinx: None","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6191/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue