## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_1$ represents the milligrams of magnesium
- $x_2$ represents the grams of carbohydrates
- $x_3$ represents the milligrams of vitamin B4
- $x_4$ represents the milligrams of vitamin A

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is $8.48x_1 + 5.91x_2 + 6.67x_3 + 5.11x_4$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $4.39x_1 \geq 0$ (implicit, as $x_1$ can be non-negative, but we focus on given constraints)
- $6.81x_2 \geq 0$ (implicit, similar to above)
- $0.77x_3 \geq 0$ (implicit, similar to above)
- $3.47x_4 \geq 0$ (implicit, similar to above)
- $4.39x_1 + 6.81x_2 \geq 50$
- $4.39x_1 + 0.77x_3 \geq 66$
- $6.81x_2 + 3.47x_4 \geq 73$
- $4.39x_1 + 6.81x_2 + 0.77x_3 + 3.47x_4 \geq 73$
- $-2x_1 + x_2 \geq 0$
- $-10x_3 + 3x_4 \geq 0$
- $4.39x_1 + 3.47x_4 \leq 366$
- $4.39x_1 + 0.77x_3 + 3.47x_4 \leq 431$
- $4.39x_1 + 6.81x_2 + 0.77x_3 \leq 435$

## 4: Formulate the problem in Gurobi
We will use Gurobi to solve this linear programming problem.

## 5: Write the Gurobi code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="milligrams_of_magnesium", lb=-gurobi.GRB.INFINITY)  # Can be negative
    x2 = model.addVar(name="grams_of_carbohydrates", lb=-gurobi.GRB.INFINITY)  # Can be negative
    x3 = model.addVar(name="milligrams_of_vitamin_B4", lb=-gurobi.GRB.INFINITY)  # Can be negative
    x4 = model.addVar(name="milligrams_of_vitamin_A", lb=-gurobi.GRB.INFINITY)  # Can be negative

    # Objective function
    model.setObjective(8.48 * x1 + 5.91 * x2 + 6.67 * x3 + 5.11 * x4, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(4.39 * x1 + 6.81 * x2 >= 50)
    model.addConstr(4.39 * x1 + 0.77 * x3 >= 66)
    model.addConstr(6.81 * x2 + 3.47 * x4 >= 73)
    model.addConstr(4.39 * x1 + 6.81 * x2 + 0.77 * x3 + 3.47 * x4 >= 73)
    model.addConstr(-2 * x1 + x2 >= 0)
    model.addConstr(-10 * x3 + 3 * x4 >= 0)
    model.addConstr(4.39 * x1 + 3.47 * x4 <= 366)
    model.addConstr(4.39 * x1 + 0.77 * x3 + 3.47 * x4 <= 431)
    model.addConstr(4.39 * x1 + 6.81 * x2 + 0.77 * x3 <= 435)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of magnesium: {x1.varValue}")
        print(f"Grams of carbohydrates: {x2.varValue}")
        print(f"Milligrams of vitamin B4: {x3.varValue}")
        print(f"Milligrams of vitamin A: {x4.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 6: Symbolic representation
```json
{
    "sym_variables": [
        ["x1", "milligrams of magnesium"],
        ["x2", "grams of carbohydrates"],
        ["x3", "milligrams of vitamin B4"],
        ["x4", "milligrams of vitamin A"]
    ],
    "objective_function": "8.48x1 + 5.91x2 + 6.67x3 + 5.11x4",
    "constraints": [
        "4.39x1 + 6.81x2 >= 50",
        "4.39x1 + 0.77x3 >= 66",
        "6.81x2 + 3.47x4 >= 73",
        "4.39x1 + 6.81x2 + 0.77x3 + 3.47x4 >= 73",
        "-2x1 + x2 >= 0",
        "-10x3 + 3x4 >= 0",
        "4.39x1 + 3.47x4 <= 366",
        "4.39x1 + 0.77x3 + 3.47x4 <= 431",
        "4.39x1 + 6.81x2 + 0.77x3 <= 435"
    ]
}
```