Creating DictEncoded in the presence of missing values

When, e.g. a `PooledArray` column, that contains missing values is converted to `DictEncoded` the dictionary is based on the result of `DataAPI.refpool`, which includes `missing`. As a result both the dictionary and the index vector contain missing values, which confuses Pandas. The missing value in the dictionary can be skipped because it is never referenced in the index vector.

```julia
julia> using Arrow, DataAPI, PooledArrays

julia> tbl = (; a = PooledArray([missing, "a", "b", "a"]))
(a = Union{Missing, String}[missing, "a", "b", "a"],)

julia> DataAPI.refarray(tbl.a)
4-element Vector{UInt32}:
0x00000001
0x00000002
0x00000003
0x00000002

julia> DataAPI.refpool(tbl.a)
3-element Vector{Union{Missing, String}}:
missing
"a"
"b"

julia> Arrow.write("tbl.arrow", tbl)
"tbl.arrow"
```

In the `read_table` result we see that there is a `null` in the dictionary at Python index 0 that is never referenced in the indices vector.

```python
$ python
Python 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:35:26) [GCC 10.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pyarrow.feather as fea
>>> fea.read_table("tbl.arrow")
pyarrow.Table
a: dictionary
----
a: [ -- dictionary:
[null,"a","b"] -- indices:
[null,1,2,1]]
>>> fea.read_feather('nyc_mv_collisions_202201.arrow')
Traceback (most recent call last):
File "", line 1, in
File "/home/bates/.julia/conda/3/lib/python3.10/site-packages/pyarrow/feather.py", line 231, in read_feather
return (read_table(
File "pyarrow/array.pxi", line 823, in pyarrow.lib._PandasConvertible.to_pandas
File "pyarrow/table.pxi", line 3913, in pyarrow.lib.Table._to_pandas
File "/home/bates/.julia/conda/3/lib/python3.10/site-packages/pyarrow/pandas_compat.py", line 818, in table_to_blockmanager
blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)
File "/home/bates/.julia/conda/3/lib/python3.10/site-packages/pyarrow/pandas_compat.py", line 1170, in _table_to_blocks
return [_reconstruct_block(item, columns, extension_columns)
File "/home/bates/.julia/conda/3/lib/python3.10/site-packages/pyarrow/pandas_compat.py", line 1170, in
return [_reconstruct_block(item, columns, extension_columns)
File "/home/bates/.julia/conda/3/lib/python3.10/site-packages/pyarrow/pandas_compat.py", line 757, in _reconstruct_block
cat = _pandas_api.categorical_type.from_codes(
File "/home/bates/.julia/conda/3/lib/python3.10/site-packages/pandas/core/arrays/categorical.py", line 687, in from_codes
dtype = CategoricalDtype._from_values_or_dtype(
File "/home/bates/.julia/conda/3/lib/python3.10/site-packages/pandas/core/dtypes/dtypes.py", line 299, in _from_values_or_dtype
dtype = CategoricalDtype(categories, ordered)
File "/home/bates/.julia/conda/3/lib/python3.10/site-packages/pandas/core/dtypes/dtypes.py", line 186, in __init__
self._finalize(categories, ordered, fastpath=False)
File "/home/bates/.julia/conda/3/lib/python3.10/site-packages/pandas/core/dtypes/dtypes.py", line 340, in _finalize
categories = self.validate_categories(categories, fastpath=fastpath)
File "/home/bates/.julia/conda/3/lib/python3.10/site-packages/pandas/core/dtypes/dtypes.py", line 534, in validate_categories
raise ValueError("Categorical categories cannot be null")
ValueError: Categorical categories cannot be null
```

One possible approach is to check for `missing` in the refpool, find its index in the refpool, delete it from the refpool and rewrite the refarray to replace that index by missing.

Start at the DictEncoded conversion and the DataAPI.refpool/refarray entry points, then reproduce the issue with Arrow.write and the PooledArray example. Check the result with pyarrow and confirm that the dictionary omits missing while indices preserve the column's missing values without causing Pandas conversion to fail.

Written by the indexing model from the issue text.