When doing a Kaplan-Meier, there are a couple of options for estimating survival function past the last observed age (which may be a censored value). One option is to keep the function at its last value, another option is to set the function to 0, and another intermediate option could be to use an exponential curve to reduce the function for the value at last observed age down to 0.

You talk about having a bigger sample size. With large data sets, assumptions are made about the location of values within the intervals (you calculate the survival function at endpoints of the intervals). One assumption that can be made is that all of the uncensored observations in an interval occur at the same value within that interval, say \(c_j\). Rather than place all probability at the \(c_j\) values, usually you evaluate the distribution function at the given endpoints and then smooth the function by interpolation -- here there is a choice about what kind of interpolation -- between successive values. I suspect SPSS and SAS are using different methods for the interpolation. My source says that linear interpolation is usual, but it seems that would not make a smooth function that people often like to make nowadays.