BUG: Fix near-minimum timestamp handling

pandas/_libs/src/vendored/numpy/datetime/np_datetime.c

@@ -483,14 +483,27 @@ npy_datetime npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT base,
   if (base == NPY_FR_ns) {
     int64_t nanoseconds;
 
-    // for near-minimum timestamps, scaling microseconds to nanoseconds
-    // overflows but adding nanoseconds puts the timestamp back in a valid range
+    // Minimum valid timestamp in nanoseconds (1677-09-21 00:12:43.145224193).
     const int64_t min_nanoseconds = NPY_MIN_INT64 + 1;
+
+    // For near-minimum timestamps (1677-09-21 00:12:43.145224193 through
+    // 1677-09-21 00:12:43.145224999), scaling microseconds to nanoseconds
+    // overflows (1677-09-21 00:12:43.145224 -> 1677-09-21 00:12:43.145224000),
+    // but adding nanoseconds can put the timestamp back in a valid range for
+    // nanosecond parts >= 193.
+
+    // (min_nanoseconds / 1000 - 1) * 1000 would overflow, so do not scale.
+    // This happens if microseconds corresponds to 1677-09-21 00:12:43.145224.
     if (microseconds == min_nanoseconds / 1000 - 1) {
-      // calculate final nanoseconds from minimum without scaling microseconds
+      // Instead, use minimum nanosecond timestamp as base and offset it with
+      // nanosecond delta between dts and the minimum (_NS_MIN_DTS.ps = 193000).
+      // If dts->ps >= _NS_MIN_DTS.ps, timestamp is at/above the minimum.
+      // If dts->ps < _NS_MIN_DTS.ps, timestamp is below minimum and overflows.
       PD_CHECK_OVERFLOW(checked_int64_add(
           min_nanoseconds, (dts->ps - _NS_MIN_DTS.ps) / 1000, &nanoseconds));
     } else {
+      // microseconds does not correspond to near-minimum timestamp, use default
+      // scaling and addition approach, handling any other overflows.
       PD_CHECK_OVERFLOW(
           scaleMicrosecondsToNanoseconds(microseconds, &nanoseconds));
       PD_CHECK_OVERFLOW(