To solve the optimization problem described, we first need to understand and translate the given information into a symbolic representation that can be used to formulate the mathematical model of the problem. The variables involved are 'milligrams of vitamin B12' and 'grams of fiber', which we will denote symbolically as `x1` and `x2`, respectively.

Given:
- Objective function: Minimize `9.9*x1 + 3.31*x2`.
- Constraints:
  - Muscle growth index from milligrams of vitamin B12 is 2.77.
  - Cognitive performance index of milligrams of vitamin B12 is 2.15.
  - Muscle growth index for grams of fiber is 2.33.
  - Grams of fiber have a cognitive performance index of 1.94.
  - Total combined muscle growth index should be at least 47: `2.77*x1 + 2.33*x2 >= 47`.
  - Total combined cognitive performance index should be at least 46: `2.15*x1 + 1.94*x2 >= 46`.
  - `-10*x1 + 9*x2 >= 0`.
  - Total combined muscle growth index should not exceed 51: `2.77*x1 + 2.33*x2 <= 51`.
  - Total combined cognitive performance index must not exceed 138: `2.15*x1 + 1.94*x2 <= 138`.

Symbolic Representation:
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B12'), ('x2', 'grams of fiber')],
    'objective_function': '9.9*x1 + 3.31*x2',
    'constraints': [
        '2.77*x1 >= 0', 
        '2.15*x1 >= 0', 
        '2.33*x2 >= 0', 
        '1.94*x2 >= 0', 
        '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'
    ]
}
```

The Gurobi code to solve this optimization problem is as follows:
```python
from gurobipy import *

# Create a new model
model = Model("Optimization_Problem")

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

# Set the objective function
model.setObjective(9.9*x1 + 3.31*x2, GRB.MINIMIZE)

# Add constraints
model.addConstr(2.77*x1 >= 0, "Muscle_Growth_Index_Vitamin_B12")
model.addConstr(2.15*x1 >= 0, "Cognitive_Performance_Index_Vitamin_B12")
model.addConstr(2.33*x2 >= 0, "Muscle_Growth_Index_Fiber")
model.addConstr(1.94*x2 >= 0, "Cognitive_Performance_Index_Fiber")
model.addConstr(2.77*x1 + 2.33*x2 >= 47, "Total_Muscle_Growth_Index_Minimum")
model.addConstr(2.15*x1 + 1.94*x2 >= 46, "Total_Cognitive_Performance_Index_Minimum")
model.addConstr(-10*x1 + 9*x2 >= 0, "Mixed_Constraint")
model.addConstr(2.77*x1 + 2.33*x2 <= 51, "Total_Muscle_Growth_Index_Maximum")
model.addConstr(2.15*x1 + 1.94*x2 <= 138, "Total_Cognitive_Performance_Index_Maximum")

# Optimize model
model.optimize()

# Print solution
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B12: {x1.x}")
    print(f"Grams of Fiber: {x2.x}")
else:
    print("No optimal solution found")
```