## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B12' and 'grams of fiber', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $9.9x_1 + 3.31x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- The muscle growth index for $x_1$ is 2.77.
- The cognitive performance index of $x_1$ is 2.15.
- The muscle growth index for $x_2$ is 2.33.
- The cognitive performance index of $x_2$ is 1.94.
- The total combined muscle growth index from $x_1$ and $x_2$ should be as much or more than 47: $2.77x_1 + 2.33x_2 \geq 47$.
- The total combined cognitive performance index from $x_1$ and $x_2$ has to be 46 at a minimum: $2.15x_1 + 1.94x_2 \geq 46$.
- $-10x_1 + 9x_2 \geq 0$.
- The total combined muscle growth index from $x_1$ and $x_2$ has to be equal to or less than 51: $2.77x_1 + 2.33x_2 \leq 51$.
- The total combined cognitive performance index from $x_1$ and $x_2$ must be 138 at a maximum: $2.15x_1 + 1.94x_2 \leq 138$.
- $x_1$ is a whole number: $x_1 \in \mathbb{Z}$.
- $x_2$ can be any real number.

## 4: Create a symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B12'), ('x2', 'grams of fiber')],
    'objective_function': '9.9*x1 + 3.31*x2',
    'constraints': [
        '2.77*x1 + 2.33*x2 >= 47',
        '2.15*x1 + 1.94*x2 >= 46',
        '-10*x1 + 9*x2 >= 0',
        '2.77*x1 + 2.33*x2 <= 51',
        '2.15*x1 + 1.94*x2 <= 138',
        'x1 >= 0'  # Assuming non-negativity for x1 and x2 as not explicitly stated otherwise
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

def optimize_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="milligrams_of_vitamin_B12", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="grams_of_fiber")

    # Objective function
    model.setObjective(9.9 * x1 + 3.31 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(2.77 * x1 + 2.33 * x2 >= 47)
    model.addConstr(2.15 * x1 + 1.94 * x2 >= 46)
    model.addConstr(-10 * x1 + 9 * x2 >= 0)
    model.addConstr(2.77 * x1 + 2.33 * x2 <= 51)
    model.addConstr(2.15 * x1 + 1.94 * x2 <= 138)

    # Non-negativity constraints (assuming x1 and x2 are non-negative)
    model.addConstr(x1 >= 0)
    model.addConstr(x2 >= 0)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B12: {x1.varValue}")
        print(f"Grams of fiber: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```