SimpleImputer can raise `TypeConversionError` if `mean` or `median` strategy used with boolean data

The following code will attempt to use the `mean` and `median` strategies with boolean data, which converts the values to floats and then imputes whatever the mean and median of the data is (which may very well be a floating point value that cannot then be converted back to BooleanNullable as the SimpleImputer currently attempts to do). Note, this is not reachable from AutoMLSearch currently, as the `Imputer` component keeps this from happening.

```python
import woodwork as ww
from evalml.pipelines.components import SimpleImputer
import pandas as pd

for strategy in ["mean", "median"]:
X_train = pd.DataFrame(
{
"fully_bool": pd.Series([True, False, True, True, True] ),
"one_nan": pd.Series([True, False, pd.NA, False, True] ),
},
)
X_train.ww.init(
logical_types={
"fully_bool": "Boolean",
"one_nan": "BooleanNullable",
},
)

imp = SimpleImputer(
impute_strategy=strategy,
)
imp.fit(X_train)
with pytest.raises(ww.exceptions.TypeConversionError, match="Error converting datatype for one_nan from type object to type boolean."):
imp.transform(X_train)
```

We should handle this situation. We have several options for how to do this:
- Explicitly disallow "mean" and "median" strategies for boolean values in the simple imputer - this would require adding logic that is, I assume, the reason we have a separate `Imputer` component in the first place
- Implicitly disallow "mean" and "median" strategies for boolean data in the simple imputer. Note in the docstring the limitations. This might also be a good time to make it more clear that this component expects all columns to be of the same type.
- Change those columns' types to Doubles in the `new_schema` prior to initializing woodwork like we do with IntegerNullable to `Double`. This doesn't make so much sense to me, as it implies a continuous relationship between boolean values, which doesn't make much sense to me, but if there's a use case for this that I'm missing, we can consider this.