Should I scale high ranging ordinal fields?
We scale data because certain algorithm will not work optimally esp. Gradient descent.
You may check the internet for further detail.
Coming to the exact question -
I am assuming, you have done the analysis that the Oridinal feature will be used as a Continuous(Not Categorical) feature with its respective values. So, I will ignore this point
Models don't see the feature scale, it looks for the respective variance, interaction with other features, Correlation with the target etc.(in a very simple language) and scaling will not hamper these parameters.
So, you must scale if that is your only concern.
 You may ignore it for some Models e.g. Decision Tree/RF will work fine even without scaling.