ENH: Change default dtype of str.get_dummies() to bool
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- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

### Overview
This PR changes the default dtype of the result from `str.get_dummies()` from `np.int64` to boolean (`bool` or `boolean`). This modification ensures consistent behavior between `pd.get_dummies()` and `str.get_dummies()`. Additionally, the implementation now adapts to the input data type to return the corresponding boolean type.

---

### Background
Currently, `pd.get_dummies()` returns a boolean dtype by default, while `str.get_dummies()` returns an integer dtype (`np.int64`). This inconsistency may cause confusion for users.

- In PR #48022, the default dtype of `pd.get_dummies()` was changed to bool.
- In PR #56291, `pd.get_dummies()` was updated to return a corresponding boolean dtype (e.g., `boolean[pyarrow]`) when the input is an `ExtensionArray`.

This PR aligns the behavior of `str.get_dummies()` with these changes.

---

### Changes Made
- Changed the default dtype of the result from `str.get_dummies()` from `np.int64` to boolean.
- Implemented support for returning the corresponding boolean type when the input data is an `ExtensionArray`. For example, when the input is of type `string[pyarrow]`, the result will be of type `boolean[pyarrow]`.

---

### Behavior Before and After the Change

#### Before
```python
>>> sr = pd.Series(["A", "B", "A"])
>>> sr.str.get_dummies()
   A  B
0  1  0
1  0  1
2  1  0
```

#### After
```python
>>> sr = pd.Series(["A", "B", "A"])
>>> sr.str.get_dummies()
       A      B
0   True  False
1  False   True
2   True  False
```

---

### Additional Notes

The following code demonstrates the difference in behavior between `pd.get_dummies()` and `str.get_dummies()` before the change:

```python
import numpy as np
import pandas as pd
import pyarrow as pa

sr_list = [pd.Series(["A", "B", "A"]),
           pd.Series(["A", "B", "A"], dtype=pd.StringDtype()),
           pd.Series(["A", "B", "A"], dtype=pd.StringDtype("pyarrow")),
           pd.Series(["A", "B", "A"], dtype=pd.StringDtype("pyarrow_numpy")),
           pd.Series(["A", "B", "A"], dtype=pd.ArrowDtype(pa.string())),
           pd.Series(["A", "B", "A"], dtype="category"),
           pd.Series(["A", "B", "A"], dtype=pd.CategoricalDtype(pd.Index(["A", "B"], dtype=pd.ArrowDtype(pa.string()))))
]

for i, sr in enumerate(sr_list):
    print(f"----- case {i}. {sr.dtype=} -----")
    print(f"pd.get_dummies: {pd.get_dummies(sr)['A'].dtype}")
    print(f"str.get_dummies: {sr.str.get_dummies()['A'].dtype}")
```

#### Output Before the Change:
(Note: In case 4, the outputs of `pd.get_dummies()` and `str.get_dummies()` already match.)

```
----- case 0. sr.dtype=dtype('O') -----
pd.get_dummies: bool
str.get_dummies: int64
----- case 1. sr.dtype=string[python] -----
pd.get_dummies: boolean
str.get_dummies: int64
----- case 2. sr.dtype=string[pyarrow] -----
pd.get_dummies: boolean
str.get_dummies: int64
----- case 3. sr.dtype=str -----
pd.get_dummies: bool
str.get_dummies: int64
----- case 4. sr.dtype=string[pyarrow] -----
pd.get_dummies: bool[pyarrow]
str.get_dummies: bool[pyarrow]
----- case 5. sr.dtype=CategoricalDtype(categories=['A', 'B'], ordered=False, categories_dtype=object) -----
pd.get_dummies: bool
str.get_dummies: int64
----- case 6. sr.dtype=CategoricalDtype(categories=['A', 'B'], ordered=False, categories_dtype=string[pyarrow]) -----
pd.get_dummies: bool[pyarrow]
str.get_dummies: int64
```

#### Output After the Change:
```
----- case 0. sr.dtype=dtype('O') -----
pd.get_dummies: bool
str.get_dummies: bool
----- case 1. sr.dtype=string[python] -----
pd.get_dummies: boolean
str.get_dummies: boolean
----- case 2. sr.dtype=string[pyarrow] -----
pd.get_dummies: boolean
str.get_dummies: boolean
----- case 3. sr.dtype=str -----
pd.get_dummies: bool
str.get_dummies: bool
----- case 4. sr.dtype=string[pyarrow] -----
pd.get_dummies: bool[pyarrow]
str.get_dummies: bool[pyarrow]
----- case 5. sr.dtype=CategoricalDtype(categories=['A', 'B'], ordered=False, categories_dtype=object) -----
pd.get_dummies: bool
str.get_dummies: bool
----- case 6. sr.dtype=CategoricalDtype(categories=['A', 'B'], ordered=False, categories_dtype=string[pyarrow]) -----
pd.get_dummies: bool[pyarrow]
str.get_dummies: bool[pyarrow]
```
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