Rules for allowed return value in combine

Currently we have:

julia> df = DataFrame(rand(2,2))
2×2 DataFrame
│ Row │ x1      │ x2        │
│     │ Float64 │ Float64   │
├─────┼─────────┼───────────┤
│ 1   │ 0.59547 │ 0.0618626 │
│ 2   │ 0.70438 │ 0.0882641 │

julia> by(df, :x1, z = :x1 => x -> rand(1,1,1))
2×2 DataFrame
│ Row │ x1      │ z          │
│     │ Float64 │ Array…     │
├─────┼─────────┼────────────┤
│ 1   │ 0.59547 │ [0.815674] │
│ 2   │ 0.70438 │ [0.603017] │

julia> by(df, :x1, z = :x1 => x -> rand(1,1))
ERROR: ArgumentError: a single value or vector result is required when passing a vector or tuple of functions (got Array{Float64,2})

This is due to the rule in:
https://github.com/JuliaData/DataFrames.jl/blob/master/src/groupeddataframe/splitapplycombine.jl#L709

I am not 100% this rule is good. I understand the original reason of disallowing them, but it seems that in Pair context it is not ambiguous and we do not need this exception.

@nalimilan - what is your opinion on thins (this is related to select design where I will post a related comment soon)

Start in src/groupeddataframe/splitapplycombine.jl around line 709 and reproduce the two Julia examples from the issue. Determine and document whether Pair results should allow matrix-valued outputs without ambiguity; the work is done when the allowed return-value rule and its behavior are agreed and covered by appropriate tests.