Update EvalML to be compatible with the new Woodwork `Boolean` inference

Woodwork updated their Boolean inference in [this](https://github.com/alteryx/woodwork/pull/1549) PR, with an additional update in [this](https://github.com/alteryx/woodwork/pull/1576) one.

This issue covers two main areas of change:
- Updating tests to be compatible
- Replacing instances of `y.ww.init()` with `ww.init_series(y)`
- Replacing expected `int64` with `bool`
- Full list [here](https://github.com/alteryx/evalml/actions/runs/3380396625/jobs/5613131642#step:7:54412)
- Updating component behaviour
- `CatBoostRegressor` - `Target value "False" cannot be parsed as float` in `fit`
- `LightGBMClassifier` and `XGBoostClassifier` - Possibly an issue with `LabelEncoder` in `predict`
- `Oversampler` - `SMOTENC` returned instead of `SMOTE` due to difference in expected categorical columns
- `ClassImbalanceDataCheck` - Reverse mapping required to return the original values instead of `True/False` in data check details/message
- `TargetLeakageDataCheck` - Issue when calculating dependence when features or target are inferred as boolean instead of int
- `partial_dependence` - Inconsistent logical types when attempting `pred = prediction_method(X_eval)`
- `TimeSeriesPipelineBase` - Attempting to use the old schema when reinitializing `y` can lead to an `incomaptible dtype` error in `_drop_time_index`
- `ClassificationPipeline` - Mapping issue in `LabelEncoder` that results in incorrect mapping when `_encode_targets` is called. The keys in the mapping dict can't be found so post mapping all values are converted to `NaN`