The SAS note https://support.sas.com/kb/37/228.html#note1 indicates that you can use the NLEstimate macro to get the risk difference. The example dataset has 100 records. In the example code, df=100. However, the macro indicates that it would typically be the number of observations minus the number of parameters estimated. So wouldn't that be 97 (100-3)?
proc logistic data=test;
class a / param=glm;
model y(event="1") = a;
lsmeans a / ilink;
store logfit;
run;
data fd;
length label f $32767;
infile datalines delimiter=',';
input label f;
datalines;
Prob Diff A1-A2,logistic(B_p1+b_p2) - logistic(B_p1+b_p3)
Prob Diff A1-A3,logistic(B_p1+b_p2) - logistic(B_p1)
Prob Diff A2-A3,logistic(B_p1+b_p3) - logistic(B_p1)
;
%NLEstimate(instore=logfit, fdata=fd, df=100)
df=, /* Specifies the degrees of freedom to be used in the */
/* test and confidence interval computed for the */
/* estimated function(s). If omitted, large-sample */
/* Wald statistics are given. The degrees of freedom */
/* for testing a linear combination of parameters in */
/* a linear model would typically be the number of */
/* observations used in fitting the model minus the */
/* number of parameters estimated in the model – */
/* essentially, the error degrees of freedom. */
