## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of calcium', 'grams of fiber', and 'grams of protein', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of calcium'
- $x_2$ for 'grams of fiber'
- $x_3$ for 'grams of protein'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $3x_1 + x_2 + 3x_3$.

## 4: List the constraints in symbolic notation
The constraints are:
- $3.18x_1 \leq 107$
- $3.69x_1 \leq 78$
- $7.56x_1 \leq 64$
- $0.52x_1 \leq 123$
- $7.91x_2 \leq 107$
- $6.97x_2 \leq 78$
- $2.11x_2 \leq 64$
- $7.81x_2 \leq 123$
- $1.06x_3 \leq 107$
- $7.41x_3 \leq 78$
- $1.96x_3 \leq 64$
- $1.78x_3 \leq 123$
- $3.18x_1 + 7.91x_2 \geq 16$
- $6.97x_2 + 7.41x_3 \geq 15$
- $7.91x_2 + 1.06x_3 \leq 57$
- $3.18x_1 + 7.91x_2 \leq 90$
- $3.18x_1 + 7.91x_2 + 1.06x_3 \leq 90$
- $3.69x_1 + 7.41x_3 \leq 37$
- $6.97x_2 + 7.41x_3 \leq 57$
- $3.69x_1 + 6.97x_2 + 7.41x_3 \leq 57$
- $7.56x_1 + 2.11x_2 \leq 35$
- $7.56x_1 + 1.96x_3 \leq 47$
- $7.56x_1 + 2.11x_2 + 1.96x_3 \leq 47$
- $0.52x_1 + 7.81x_2 \leq 89$
- $0.52x_1 + 1.78x_3 \leq 89$
- $0.52x_1 + 7.81x_2 + 1.78x_3 \leq 89$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of calcium'), 
        ('x2', 'grams of fiber'), 
        ('x3', 'grams of protein')
    ], 
    'objective_function': '3*x1 + x2 + 3*x3', 
    'constraints': [
        '3.18*x1 <= 107',
        '3.69*x1 <= 78',
        '7.56*x1 <= 64',
        '0.52*x1 <= 123',
        '7.91*x2 <= 107',
        '6.97*x2 <= 78',
        '2.11*x2 <= 64',
        '7.81*x2 <= 123',
        '1.06*x3 <= 107',
        '7.41*x3 <= 78',
        '1.96*x3 <= 64',
        '1.78*x3 <= 123',
        '3.18*x1 + 7.91*x2 >= 16',
        '6.97*x2 + 7.41*x3 >= 15',
        '7.91*x2 + 1.06*x3 <= 57',
        '3.18*x1 + 7.91*x2 <= 90',
        '3.18*x1 + 7.91*x2 + 1.06*x3 <= 90',
        '3.69*x1 + 7.41*x3 <= 37',
        '6.97*x2 + 7.41*x3 <= 57',
        '3.69*x1 + 6.97*x2 + 7.41*x3 <= 57',
        '7.56*x1 + 2.11*x2 <= 35',
        '7.56*x1 + 1.96*x3 <= 47',
        '7.56*x1 + 2.11*x2 + 1.96*x3 <= 47',
        '0.52*x1 + 7.81*x2 <= 89',
        '0.52*x1 + 1.78*x3 <= 89',
        '0.52*x1 + 7.81*x2 + 1.78*x3 <= 89'
    ]
}
```

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

def optimize():
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="milligrams_of_calcium", lb=-gurobi.GRB.INFINITY)
    x2 = model.addVar(name="grams_of_fiber", lb=-gurobi.GRB.INFINITY)
    x3 = model.addVar(name="grams_of_protein", lb=-gurobi.GRB.INFINITY)

    # Objective function
    model.setObjective(3*x1 + x2 + 3*x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(3.18*x1 <= 107)
    model.addConstr(3.69*x1 <= 78)
    model.addConstr(7.56*x1 <= 64)
    model.addConstr(0.52*x1 <= 123)

    model.addConstr(7.91*x2 <= 107)
    model.addConstr(6.97*x2 <= 78)
    model.addConstr(2.11*x2 <= 64)
    model.addConstr(7.81*x2 <= 123)

    model.addConstr(1.06*x3 <= 107)
    model.addConstr(7.41*x3 <= 78)
    model.addConstr(1.96*x3 <= 64)
    model.addConstr(1.78*x3 <= 123)

    model.addConstr(3.18*x1 + 7.91*x2 >= 16)
    model.addConstr(6.97*x2 + 7.41*x3 >= 15)
    model.addConstr(7.91*x2 + 1.06*x3 <= 57)
    model.addConstr(3.18*x1 + 7.91*x2 <= 90)
    model.addConstr(3.18*x1 + 7.91*x2 + 1.06*x3 <= 90)

    model.addConstr(3.69*x1 + 7.41*x3 <= 37)
    model.addConstr(6.97*x2 + 7.41*x3 <= 57)
    model.addConstr(3.69*x1 + 6.97*x2 + 7.41*x3 <= 57)

    model.addConstr(7.56*x1 + 2.11*x2 <= 35)
    model.addConstr(7.56*x1 + 1.96*x3 <= 47)
    model.addConstr(7.56*x1 + 2.11*x2 + 1.96*x3 <= 47)

    model.addConstr(0.52*x1 + 7.81*x2 <= 89)
    model.addConstr(0.52*x1 + 1.78*x3 <= 89)
    model.addConstr(0.52*x1 + 7.81*x2 + 1.78*x3 <= 89)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("milligrams of calcium: ", x1.varValue)
        print("grams of fiber: ", x2.varValue)
        print("grams of protein: ", x3.varValue)
    else:
        print("The model is infeasible")

optimize()
```