## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_1$ represents milligrams of potassium
- $x_2$ represents milligrams of vitamin B5
- $x_3$ represents milligrams of vitamin B3
- $x_4$ represents grams of fat
- $x_5$ represents grams of fiber

## Step 2: Define the objective function in symbolic notation
The objective function to maximize is: $7.39x_1 + 7.48x_2 + 4.63x_3 + 5.2x_4 + 9.28x_5$

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $4.7x_1 \leq 129$
- $8.01x_2 \leq 129$
- $6.64x_3 \leq 129$
- $6.6x_4 \leq 129$
- $6.16x_5 \leq 129$
- $8.01x_2 + 6.6x_4 \geq 21$
- $6.64x_3 + 6.6x_4 \geq 22$
- $6.6x_4 + 6.16x_5 \geq 8$
- $4.7x_1 + 8.01x_2 \geq 24$
- $6.64x_3 + 6.16x_5 \geq 17$
- $4.7x_1 + 6.64x_3 \geq 18$
- $4.7x_1 + 6.16x_5 \geq 24$
- $6.64x_3 + 6.16x_5 \leq 38$
- $6.64x_3 + 6.6x_4 \leq 81$
- $8.01x_2 + 6.6x_4 \leq 54$
- $4.7x_1 + 6.16x_5 \leq 75$
- $4.7x_1 + 8.01x_2 \leq 31$
- $4.7x_1 + 6.6x_4 \leq 121$
- $4.7x_1 + 8.01x_2 + 6.64x_3 + 6.6x_4 + 6.16x_5 \leq 121$

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

# Define the model
m = gp.Model()

# Define the variables
x1 = m.addVar(name="milligrams of potassium", lb=-gp.GRB.INFINITY)
x2 = m.addVar(name="milligrams of vitamin B5", lb=-gp.GRB.INFINITY)
x3 = m.addVar(name="milligrams of vitamin B3", lb=-gp.GRB.INFINITY)
x4 = m.addVar(name="grams of fat", lb=-gp.GRB.INFINITY)
x5 = m.addVar(name="grams of fiber", lb=-gp.GRB.INFINITY)

# Define the objective function
m.setObjective(7.39*x1 + 7.48*x2 + 4.63*x3 + 5.2*x4 + 9.28*x5, gp.GRB.MAXIMIZE)

# Define the constraints
m.addConstr(4.7*x1 <= 129)
m.addConstr(8.01*x2 <= 129)
m.addConstr(6.64*x3 <= 129)
m.addConstr(6.6*x4 <= 129)
m.addConstr(6.16*x5 <= 129)

m.addConstr(8.01*x2 + 6.6*x4 >= 21)
m.addConstr(6.64*x3 + 6.6*x4 >= 22)
m.addConstr(6.6*x4 + 6.16*x5 >= 8)
m.addConstr(4.7*x1 + 8.01*x2 >= 24)
m.addConstr(6.64*x3 + 6.16*x5 >= 17)
m.addConstr(4.7*x1 + 6.64*x3 >= 18)
m.addConstr(4.7*x1 + 6.16*x5 >= 24)
m.addConstr(6.64*x3 + 6.16*x5 <= 38)
m.addConstr(6.64*x3 + 6.6*x4 <= 81)
m.addConstr(8.01*x2 + 6.6*x4 <= 54)
m.addConstr(4.7*x1 + 6.16*x5 <= 75)
m.addConstr(4.7*x1 + 8.01*x2 <= 31)
m.addConstr(4.7*x1 + 6.6*x4 <= 121)
m.addConstr(4.7*x1 + 8.01*x2 + 6.64*x3 + 6.6*x4 + 6.16*x5 <= 121)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Milligrams of potassium: ", x1.varValue)
    print("Milligrams of vitamin B5: ", x2.varValue)
    print("Milligrams of vitamin B3: ", x3.varValue)
    print("Grams of fat: ", x4.varValue)
    print("Grams of fiber: ", x5.varValue)
    print("Objective function value: ", m.objVal)
else:
    print("No optimal solution found.")
```

## 5: Symbolic Representation
```json
{
    "sym_variables": [
        ["x1", "milligrams of potassium"],
        ["x2", "milligrams of vitamin B5"],
        ["x3", "milligrams of vitamin B3"],
        ["x4", "grams of fat"],
        ["x5", "grams of fiber"]
    ],
    "objective_function": "7.39*x1 + 7.48*x2 + 4.63*x3 + 5.2*x4 + 9.28*x5",
    "constraints": [
        "4.7*x1 <= 129",
        "8.01*x2 <= 129",
        "6.64*x3 <= 129",
        "6.6*x4 <= 129",
        "6.16*x5 <= 129",
        "8.01*x2 + 6.6*x4 >= 21",
        "6.64*x3 + 6.6*x4 >= 22",
        "6.6*x4 + 6.16*x5 >= 8",
        "4.7*x1 + 8.01*x2 >= 24",
        "6.64*x3 + 6.16*x5 >= 17",
        "4.7*x1 + 6.64*x3 >= 18",
        "4.7*x1 + 6.16*x5 >= 24",
        "6.64*x3 + 6.16*x5 <= 38",
        "6.64*x3 + 6.6*x4 <= 81",
        "8.01*x2 + 6.6*x4 <= 54",
        "4.7*x1 + 6.16*x5 <= 75",
        "4.7*x1 + 8.01*x2 <= 31",
        "4.7*x1 + 6.6*x4 <= 121",
        "4.7*x1 + 8.01*x2 + 6.64*x3 + 6.6*x4 + 6.16*x5 <= 121"
    ]
}
```