>       assert df2['a'].dtype == df['a'].dtype
E       AssertionError: assert dtype('O') == Int64Dtype()
E        +  where dtype('O') = 0    1\n1    2\nName: a, dtype: object.dtype
E        +  and   Int64Dtype() = Series([], Name: a, dtype: Int64).dtype

def _get_empty_dtype(join_units: Sequence[JoinUnit]) -> DtypeObj:
    ...
    dtypes = [unit.dtype for unit in join_units if not unit.is_na]
    if not len(dtypes):
        dtypes = [unit.dtype for unit in join_units if unit.block.dtype.kind != "V"]

    dtype = find_common_type(dtypes)
    ...

def _concatenate_join_units(
    join_units: list[JoinUnit], concat_axis: int, copy: bool
) -> ArrayLike:
    """
    Concatenate values from several join units along selected axis.
    """
    if concat_axis == 0 and len(join_units) > 1:
        # Concatenating join units along ax0 is handled in _merge_blocks.
        raise AssertionError("Concatenating join units along axis0")

    empty_dtype = _get_empty_dtype(join_units)

    has_none_blocks = any(unit.block.dtype.kind == "V" for unit in join_units)
    upcasted_na = _dtype_to_na_value(empty_dtype, has_none_blocks)

    to_concat = [
        ju.get_reindexed_values(empty_dtype=empty_dtype, upcasted_na=upcasted_na)
        for ju in join_units
    ]

    if len(to_concat) == 1:
        # Only one block, nothing to concatenate.
        concat_values = to_concat[0]
        if copy:
            if isinstance(concat_values, np.ndarray):
                # non-reindexed (=not yet copied) arrays are made into a view
                # in JoinUnit.get_reindexed_values
                if concat_values.base is not None:
                    concat_values = concat_values.copy()
            else:
                concat_values = concat_values.copy()

    elif any(is_1d_only_ea_obj(t) for t in to_concat):  # <-- this branch isn't entered
        # TODO(EA2D): special case not needed if all EAs used HybridBlocks
        # NB: we are still assuming here that Hybrid blocks have shape (1, N)
        # concatting with at least one EA means we are concatting a single column
        # the non-EA values are 2D arrays with shape (1, n)

        # error: No overload variant of "__getitem__" of "ExtensionArray" matches
        # argument type "Tuple[int, slice]"
        to_concat = [
            t if is_1d_only_ea_obj(t) else t[0, :]  # type: ignore[call-overload]
            for t in to_concat
        ]
        concat_values = concat_compat(to_concat, axis=0, ea_compat_axis=True)
        concat_values = ensure_block_shape(concat_values, 2)

    else:
        concat_values = concat_compat(to_concat, axis=concat_axis)

    return concat_values