Why does SQL Server not do compound column statistics histograms?
Background
The current SQL Server model only uses single-column histograms and multi-column density information. Single column histograms are used to estimate selectivity for suitable predicates e.g. a = 1 or b &gt; 50. A query with multiple predicates simply combines the individual selectivities (with assumptions) to produce an estimated overall selectivity.
For an example, see my article Cardinality Estimation: Combining Density Statistics
Multi-column density further informs the model by providing weak correlation information for multiple equality predicates and grouping cardinalities for aggregations.
Statistics associated with indexes are an opportunistic add-on to that model: The engine might as well collect (normally full scan) statistics while it is building an index. SQL Server automatically constructs a leading-column histogram and density information for the other keys.
Histograms for non-leading columns in an index may be constructed on demand automatically by the query processor, or in advance using sp_createstats with the @indexonly option (among others).
Multi-column histograms
The assumptions made when combining single-column statistics (as above) may or may not model the reality of the data well enough. In many cases, the options available (exponential backoff, independence, minimum selectivity) produce a 'good enough' estimate.
We also have filtered statistics (and indexes) as a natural solution to low-cardinality leading column indexes such as in the question example. Taking these to the logical extreme leads us closer to the multi-dimensional statistics that the question is not about.
When the available modelling options cannot provide a suitable estimate, a multi-column statistic histogram could indeed give a better selectivity estimate for suitable index predicates, in some cases. There are some difficulties around combining different data types in different columns, but nothing insurmountable.
We would also need a histogram for each level of the index keys (for best results); so for an index on (a, b, c) that would mean histograms on (a, b) and (a, b, c) in addition to the current single-column histogram on (a) alone.
The mechanism used to detect stale statistics would also need to be modified to maintain affected multi-column histograms. These histograms would likely end up being rebuilt more often than single-column statistics, simply because modifications to more columns affect them.
All this adds size, complexity, and maintenance overhead.
Multi-column statistics can be simulated (to a limited extent) using a statistic created on a carefully constructed computed column referencing multiple columns. The query would need to include a predicate on the computed column (or an exact textual match for the underlying formula) to take advantage of that statistic. There are probably only very limited situations where this approach is practical. Nevertheless, it has some of the same implementation issues as automatic multi-column histograms would.
Ultimately, the only people that could say for sure why SQL Server does not support multi-column statistics would be the designers themselves. If you feel you can make a strong case for a product improvement in this area with wide applicability, you could suggest it on Connect or via your normal support channel.
Footnote

In this case, it makes the whole histogram a lot less useful, because the first column only ever has two values

The histogram still provides useful information about the distribution of values in the leading column: When the statistics were built, there were 24,398 rows where IsArchived was false, and 216,602 rows where is was true.
In addition, the statistics object tells us there are (1 / 0.5) = 2 distinct values for IsArchived, (1 / 4.149378E-06) ~= 241000 distinct values for (IsArchived, Mystery) with an average row size of 37 bytes, and there same frequency for (IsArchived, Mystery, Id) with 4 extra bytes per row.
That's all good general-purpose information, which can be combined with statistical information about other columns to produce a selectivity estimate in queries with multiple predicates (as mentioned).