## Step 1: Define the symbolic representation of the variables
The variables are 'grams of carbohydrates', 'milligrams of magnesium', 'milligrams of calcium', and 'milligrams of vitamin B4'. Let's denote them as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : grams of carbohydrates
- $x_1$ : milligrams of magnesium
- $x_2$ : milligrams of calcium
- $x_3$ : milligrams of vitamin B4

## 3: Define the objective function in symbolic notation
The objective function to minimize is $2x_0 + 3x_1 + 3x_2 + 4x_3$.

## 4: List the constraints in symbolic notation
The constraints are:
1. $x_0 + 7x_1 \geq 10$
2. $x_0 + 2x_3 \geq 8$
3. $x_0 + 4x_2 \geq 5$
4. $x_0 + 7x_1 + 4x_2 + 2x_3 \geq 5$
5. $x_0 + x_3 \geq 7$
6. $x_0 + 6x_1 \geq 11$
7. $4x_2 + x_3 \geq 13$
8. $6x_1 + x_3 \geq 7$
9. $6x_1 + 4x_2 + x_3 \geq 13$
10. $x_0 + 6x_1 + 4x_2 + x_3 \geq 13$
11. $7x_0 - 4x_2 \geq 0$
12. $-2x_1 + 2x_2 \geq 0$
13. $x_0 + 7x_1 + 2x_3 \leq 46$
14. $x_0 + 4x_2 + 2x_3 \leq 29$

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x0', 'grams of carbohydrates'), 
        ('x1', 'milligrams of magnesium'), 
        ('x2', 'milligrams of calcium'), 
        ('x3', 'milligrams of vitamin B4')
    ], 
    'objective_function': '2*x0 + 3*x1 + 3*x2 + 4*x3', 
    'constraints': [
        'x0 + 7*x1 >= 10',
        'x0 + 2*x3 >= 8',
        'x0 + 4*x2 >= 5',
        'x0 + 7*x1 + 4*x2 + 2*x3 >= 5',
        'x0 + x3 >= 7',
        'x0 + 6*x1 >= 11',
        '4*x2 + x3 >= 13',
        '6*x1 + x3 >= 7',
        '6*x1 + 4*x2 + x3 >= 13',
        'x0 + 6*x1 + 4*x2 + x3 >= 13',
        '7*x0 - 4*x2 >= 0',
        '-2*x1 + 2*x2 >= 0',
        'x0 + 7*x1 + 2*x3 <= 46',
        'x0 + 4*x2 + 2*x3 <= 29'
    ]
}
```

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

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

    # Define the variables
    x0 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x0")  # grams of carbohydrates
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x1")  # milligrams of magnesium
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x2")  # milligrams of calcium
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x3")  # milligrams of vitamin B4

    # Define the objective function
    model.setObjective(2 * x0 + 3 * x1 + 3 * x2 + 4 * x3, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(x0 + 7 * x1 >= 10)
    model.addConstr(x0 + 2 * x3 >= 8)
    model.addConstr(x0 + 4 * x2 >= 5)
    model.addConstr(x0 + 7 * x1 + 4 * x2 + 2 * x3 >= 5)
    model.addConstr(x0 + x3 >= 7)
    model.addConstr(x0 + 6 * x1 >= 11)
    model.addConstr(4 * x2 + x3 >= 13)
    model.addConstr(6 * x1 + x3 >= 7)
    model.addConstr(6 * x1 + 4 * x2 + x3 >= 13)
    model.addConstr(x0 + 6 * x1 + 4 * x2 + x3 >= 13)
    model.addConstr(7 * x0 - 4 * x2 >= 0)
    model.addConstr(-2 * x1 + 2 * x2 >= 0)
    model.addConstr(x0 + 7 * x1 + 2 * x3 <= 46)
    model.addConstr(x0 + 4 * x2 + 2 * x3 <= 29)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"x0: {x0.varValue}")
        print(f"x1: {x1.varValue}")
        print(f"x2: {x2.varValue}")
        print(f"x3: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```