## Step 1: Define the symbolic representation of the variables
The variables are defined as follows:
- $x_0$ : milligrams of magnesium
- $x_1$ : grams of carbohydrates
- $x_2$ : grams of fat
- $x_3$ : milligrams of vitamin C

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is: $4.55x_0 + 1.85x_1 + 5.52x_2 + 5.81x_3$

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $29x_0 \leq 145$
- $23x_0 \leq 129$
- $14x_1 \leq 145$ is not given, but $14x_1$ is part of other constraints
- $4x_1 \leq 129$ is not given, but $4x_1$ is part of other constraints
- $10x_2 \leq 145$ is not given, but $10x_2$ is part of other constraints
- $31x_2 \leq 129$ is not given, but $31x_2$ is part of other constraints
- $27x_3 \leq 145$
- $13x_3 \leq 129$
- $10x_2 + 27x_3 \geq 25$
- $29x_0 + 10x_2 \geq 33$
- $14x_1 + 10x_2 \geq 21$
- $29x_0 + 14x_1 \geq 19$
- $29x_0 + 14x_1 + 10x_2 + 27x_3 \geq 19$
- $31x_2 + 13x_3 \geq 22$
- $23x_0 + 4x_1 + 31x_2 \geq 30$
- $4x_1 + 31x_2 + 13x_3 \geq 30$
- $23x_0 + 31x_2 + 13x_3 \geq 30$
- $23x_0 + 4x_1 + 31x_2 \geq 28$
- $4x_1 + 31x_2 + 13x_3 \geq 28$
- $23x_0 + 31x_2 + 13x_3 \geq 28$
- $23x_0 + 4x_1 + 31x_2 \geq 27$
- $4x_1 + 31x_2 + 13x_3 \geq 27$
- $23x_0 + 31x_2 + 13x_3 \geq 27$
- $23x_0 + 4x_1 + 31x_2 + 13x_3 \geq 27$
- $-6x_0 + 10x_2 \geq 0$
- $29x_0 + 10x_2 \leq 141$
- $29x_0 + 27x_3 \leq 145$
- $10x_2 + 27x_3 \leq 48$
- $29x_0 + 14x_1 + 27x_3 \leq 45$
- $14x_1 + 10x_2 + 27x_3 \leq 115$
- $23x_0 + 13x_3 \leq 102$
- $23x_0 + 31x_2 \leq 37$

## Step 4: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of magnesium'), 
        ('x1', 'grams of carbohydrates'), 
        ('x2', 'grams of fat'), 
        ('x3', 'milligrams of vitamin C')
    ], 
    'objective_function': '4.55*x0 + 1.85*x1 + 5.52*x2 + 5.81*x3', 
    'constraints': [
        '29*x0 <= 145',
        '23*x0 <= 129',
        '10*x2 + 27*x3 >= 25',
        '29*x0 + 10*x2 >= 33',
        '14*x1 + 10*x2 >= 21',
        '29*x0 + 14*x1 >= 19',
        '29*x0 + 14*x1 + 10*x2 + 27*x3 >= 19',
        '31*x2 + 13*x3 >= 22',
        '23*x0 + 4*x1 + 31*x2 >= 30',
        '4*x1 + 31*x2 + 13*x3 >= 30',
        '23*x0 + 31*x2 + 13*x3 >= 30',
        '23*x0 + 4*x1 + 31*x2 >= 28',
        '4*x1 + 31*x2 + 13*x3 >= 28',
        '23*x0 + 31*x2 + 13*x3 >= 28',
        '23*x0 + 4*x1 + 31*x2 >= 27',
        '4*x1 + 31*x2 + 13*x3 >= 27',
        '23*x0 + 31*x2 + 13*x3 >= 27',
        '23*x0 + 4*x1 + 31*x2 + 13*x3 >= 27',
        '-6*x0 + 10*x2 >= 0',
        '29*x0 + 10*x2 <= 141',
        '29*x0 + 27*x3 <= 145',
        '10*x2 + 27*x3 <= 48',
        '29*x0 + 14*x1 + 27*x3 <= 45',
        '14*x1 + 10*x2 + 27*x3 <= 115',
        '23*x0 + 13*x3 <= 102',
        '23*x0 + 31*x2 <= 37'
    ]
}
```

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

# Define the model
m = gp.Model()

# Define the variables
x0 = m.addVar(name="milligrams of magnesium", lb=-gp.GRB.INFINITY)
x1 = m.addVar(name="grams of carbohydrates", lb=-gp.GRB.INFINITY)
x2 = m.addVar(name="grams of fat", lb=-gp.GRB.INFINITY)
x3 = m.addVar(name="milligrams of vitamin C", lb=-gp.GRB.INFINITY)

# Define the objective function
m.setObjective(4.55 * x0 + 1.85 * x1 + 5.52 * x2 + 5.81 * x3, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(29 * x0 <= 145)
m.addConstr(23 * x0 <= 129)
m.addConstr(10 * x2 + 27 * x3 >= 25)
m.addConstr(29 * x0 + 10 * x2 >= 33)
m.addConstr(14 * x1 + 10 * x2 >= 21)
m.addConstr(29 * x0 + 14 * x1 >= 19)
m.addConstr(29 * x0 + 14 * x1 + 10 * x2 + 27 * x3 >= 19)
m.addConstr(31 * x2 + 13 * x3 >= 22)
m.addConstr(23 * x0 + 4 * x1 + 31 * x2 >= 30)
m.addConstr(4 * x1 + 31 * x2 + 13 * x3 >= 30)
m.addConstr(23 * x0 + 31 * x2 + 13 * x3 >= 30)
m.addConstr(23 * x0 + 4 * x1 + 31 * x2 >= 28)
m.addConstr(4 * x1 + 31 * x2 + 13 * x3 >= 28)
m.addConstr(23 * x0 + 31 * x2 + 13 * x3 >= 28)
m.addConstr(23 * x0 + 4 * x1 + 31 * x2 >= 27)
m.addConstr(4 * x1 + 31 * x2 + 13 * x3 >= 27)
m.addConstr(23 * x0 + 31 * x2 + 13 * x3 >= 27)
m.addConstr(23 * x0 + 4 * x1 + 31 * x2 + 13 * x3 >= 27)
m.addConstr(-6 * x0 + 10 * x2 >= 0)
m.addConstr(29 * x0 + 10 * x2 <= 141)
m.addConstr(29 * x0 + 27 * x3 <= 145)
m.addConstr(10 * x2 + 27 * x3 <= 48)
m.addConstr(29 * x0 + 14 * x1 + 27 * x3 <= 45)
m.addConstr(14 * x1 + 10 * x2 + 27 * x3 <= 115)
m.addConstr(23 * x0 + 13 * x3 <= 102)
m.addConstr(23 * x0 + 31 * x2 <= 37)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of magnesium: {x0.varValue}")
    print(f"Grams of carbohydrates: {x1.varValue}")
    print(f"Grams of fat: {x2.varValue}")
    print(f"Milligrams of vitamin C: {x3.varValue}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```