Extremely high MSE/MAE for Ridge Regression(sklearn) when the label is directly calculated from the features
(To summarize the comment thread into an answer)

Your original scores:

Mean Absolute Error: 37216342513.01034
Root Mean Squared Error: 871869805169.7842


are based on the original-scale target variable and are between $10^{10}$ and $10^{12}$, at least significantly smaller than the mean of the features (and the target)?  So these aren't automatically bad scores, although for a perfect relationship we should hope for better.  Furthermore, a 0.69 R2 values is pretty low, no scale-consciousness needed.

That both of the model's coefficients came out positive is the most worrisome point.  I'm glad you identified the culprit rows; I don't know how I would have diagnosed that from here.

Your new ridge regression still has "large" errors, but significantly smaller than before, and quite small compared to the feature/target scale.  And now the coefficients have different signs.  (I think if you'd left the TransformedTargetRegressor in, you'd get largely the same results, but with less penalization.)

Finally, when such an exact relationship is the truth, it makes sense not to penalize the regression.  Your coefficients here are a little bit larger, and the errors drop away to nearly nothing, especially considering the scale of the target.