[Python] date64 arrays do not round-trip through pandas conversion

### Describe the bug, including details regarding any error messages, version, and platform.

In PyArrow, Date64Array values do not maintain precision when being loaded from pandas by `pa.array`.

For example, let's make a date64 array value, and convert it to a pandas Series, taking care to avoid using datetime objects:

```py
import pyarrow as pa
import pandas as pd
date64_array = pa.array([1, 2, 3], pa.date64())
date64_pd = date64_array.to_pandas(date_as_object=False)

# Now load it back in:
date64_roundtripped = pa.array(date64_pd, pa.date64())

# It ought to be unchanged - but its not, this assertion fails:
assert date64_roundtripped == date64_array
```
If one prints `pc.subtract(date64_roundtripped, date64_array)`, you can see that they are different:
```

[
-1,
-2,
-3
]
```

Note that this does not occur for date32:
```py
import pyarrow as pa
import pandas as pd
date32_array = pa.array([1, 2, 3], pa.date32())
date32_pd = date32_array.to_pandas(date_as_object=False)

date32_roundtripped = pa.array(pandas, pa.date32())

# just fine:
assert date32_roundtripped == date32_array
```

It appears to me that `date64_pd` is just fine. It prints as this:
```
0 1970-01-01 00:00:00.001
1 1970-01-01 00:00:00.002
2 1970-01-01 00:00:00.003
dtype: datetime64[ns]
```

One hint at whats going on is to use `pa.Array.from_pandas`. That actually returns a `TimestampArray:
```
In [31]: pa.Array.from_pandas(date64_array)
Out[31]:

[
1970-01-01 00:00:00.001000000,
1970-01-01 00:00:00.002000000,
1970-01-01 00:00:00.003000000
]
```

The issue _might_ be that conversion from TimestampArray to Date64 array drops precision, maybe.

### Component(s)

Python