Putting dirty data back into a model
This question has no general answer; it depends on the specifics of both the statistical method in question and the implementation of that method in whatever language you used.

For example if you used a logistic GLM, then a missing value is an error since data records must be complete. Presumably you would have included a default value, and you will have to manually apply that if you want those records to be scored.

Alternatively, if you were using gradient boosted trees (gbm), then they can cope with missing values, but do so in an implementation-defined way. The answers would be different in the gbm and xgboost R packages, for example.