How does PCA and clustering treat different type of predictors where order of their values signify the result differently?
Similar combinations of values will be clustered together regardless of their meaning in your model.  The meaning of the clusters is for the model builder to determine.

You could engineer another feature (call it ‘goodness’) that takes the GDP and divides by child mortality (for instance) in order to add more variables to determine clusters, by dividing GDP by mortality counties with lower GDP and higher mortality rates would have a smaller “goodness” value while higher GDP and lower mortality would have higher “goodness” values and then would give the model a better chance to cluster on this factor.