## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'grams of fat', 'milligrams of potassium', 'milligrams of vitamin C', and 'milligrams of calcium', which we can denote as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $3x_0 + 3x_1 + 7x_2 + x_3$.

## 3: List the constraints in symbolic notation
The constraints given can be translated into the following:
- $17x_0 \leq 454$
- $19x_0 \leq 235$
- $16x_0 \leq 527$
- $9x_0 \leq 397$
- $16x_1 \leq 454$
- $16x_1 \leq 235$
- $7x_1 \leq 527$
- $16x_1 \leq 397$
- $15x_2 \leq 454$
- $19x_2 \leq 235$
- $8x_2 \leq 527$
- $3x_2 \leq 397$
- $2x_3 \leq 454$
- $18x_3 \leq 235$
- $6x_3 \leq 527$
- $17x_3 \leq 397$
- $17x_0 + 2x_3 \geq 55$
- $16x_1 + 2x_3 \geq 62$
- $19x_0 + 18x_3 \geq 48$
- $19x_2 + 18x_3 \geq 37$
- $19x_0 + 16x_1 + 18x_3 \geq 41$
- $19x_0 + 16x_1 + 19x_2 \geq 41$
- $9x_0 + 3x_2 \geq 57$
- $9x_0 + 16x_1 \geq 57$
- $16x_1 + 3x_2 \geq 68$
- $3x_2 + 17x_3 \geq 40$
- $6x_0 + 8x_2 - 4x_3 \geq 0$
- $15x_2 + 2x_3 \leq 360$
- $17x_0 + 16x_1 \leq 446$
- $17x_0 + 2x_3 \leq 335$
- $17x_0 + 15x_2 + 2x_3 \leq 316$
- $17x_0 + 16x_1 + 15x_2 + 2x_3 \leq 316$
- $19x_2 + 18x_3 \leq 140$
- $19x_0 + 16x_1 \leq 118$
- $16x_1 + 19x_2 \leq 86$
- $19x_0 + 16x_1 + 19x_2 + 18x_3 \leq 86$
- $16x_0 + 8x_2 \leq 405$
- $7x_1 + 6x_3 \leq 289$
- $7x_1 + 8x_2 \leq 448$
- $16x_0 + 7x_1 \leq 427$
- $16x_0 + 6x_3 \leq 481$
- $16x_0 + 7x_1 + 8x_2 + 6x_3 \leq 481$
- $9x_0 + 17x_3 \leq 283$
- $16x_1 + 3x_2 \leq 332$
- $9x_0 + 16x_1 \leq 173$
- $16x_1 + 17x_3 \leq 383$
- $16x_1 + 3x_2 + 17x_3 \leq 361$
- $9x_0 + 16x_1 + 3x_2 + 17x_3 \leq 361$

## 4: Create the Gurobi model and variables
We will now create a Gurobi model and define the variables.

## 5: Implement the objective function and constraints in Gurobi
```python
import gurobi as gp

# Create a new model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="x0", lb=0)  # grams of fat
x1 = m.addVar(name="x1", lb=0)  # milligrams of potassium
x2 = m.addVar(name="x2", lb=0)  # milligrams of vitamin C
x3 = m.addVar(name="x3", lb=0)  # milligrams of calcium

# Objective function
m.setObjective(3*x0 + 3*x1 + 7*x2 + x3, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(17*x0 <= 454)
m.addConstr(19*x0 <= 235)
m.addConstr(16*x0 <= 527)
m.addConstr(9*x0 <= 397)

m.addConstr(16*x1 <= 454)
m.addConstr(16*x1 <= 235)
m.addConstr(7*x1 <= 527)
m.addConstr(16*x1 <= 397)

m.addConstr(15*x2 <= 454)
m.addConstr(19*x2 <= 235)
m.addConstr(8*x2 <= 527)
m.addConstr(3*x2 <= 397)

m.addConstr(2*x3 <= 454)
m.addConstr(18*x3 <= 235)
m.addConstr(6*x3 <= 527)
m.addConstr(17*x3 <= 397)

m.addConstr(17*x0 + 2*x3 >= 55)
m.addConstr(16*x1 + 2*x3 >= 62)
m.addConstr(19*x0 + 18*x3 >= 48)
m.addConstr(19*x2 + 18*x3 >= 37)
m.addConstr(19*x0 + 16*x1 + 18*x3 >= 41)
m.addConstr(19*x0 + 16*x1 + 19*x2 >= 41)

m.addConstr(9*x0 + 3*x2 >= 57)
m.addConstr(9*x0 + 16*x1 >= 57)
m.addConstr(16*x1 + 3*x2 >= 68)
m.addConstr(3*x2 + 17*x3 >= 40)

m.addConstr(6*x0 + 8*x2 - 4*x3 >= 0)

m.addConstr(15*x2 + 2*x3 <= 360)
m.addConstr(17*x0 + 16*x1 <= 446)
m.addConstr(17*x0 + 2*x3 <= 335)
m.addConstr(17*x0 + 15*x2 + 2*x3 <= 316)
m.addConstr(17*x0 + 16*x1 + 15*x2 + 2*x3 <= 316)

m.addConstr(19*x2 + 18*x3 <= 140)
m.addConstr(19*x0 + 16*x1 <= 118)
m.addConstr(16*x1 + 19*x2 <= 86)
m.addConstr(19*x0 + 16*x1 + 19*x2 + 18*x3 <= 86)

m.addConstr(16*x0 + 8*x2 <= 405)
m.addConstr(7*x1 + 6*x3 <= 289)
m.addConstr(7*x1 + 8*x2 <= 448)
m.addConstr(16*x0 + 7*x1 <= 427)
m.addConstr(16*x0 + 6*x3 <= 481)
m.addConstr(16*x0 + 7*x1 + 8*x2 + 6*x3 <= 481)

m.addConstr(9*x0 + 17*x3 <= 283)
m.addConstr(16*x1 + 3*x2 <= 332)
m.addConstr(9*x0 + 16*x1 <= 173)
m.addConstr(16*x1 + 17*x3 <= 383)
m.addConstr(16*x1 + 3*x2 + 17*x3 <= 361)
m.addConstr(9*x0 + 16*x1 + 3*x2 + 17*x3 <= 361)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("x0: ", x0.varValue)
    print("x1: ", x1.varValue)
    print("x2: ", x2.varValue)
    print("x3: ", x3.varValue)
else:
    print("The model is infeasible")
```

```json
{
    "sym_variables": [
        ["x0", "grams of fat"],
        ["x1", "milligrams of potassium"],
        ["x2", "milligrams of vitamin C"],
        ["x3", "milligrams of calcium"]
    ],
    "objective_function": "3*x0 + 3*x1 + 7*x2 + x3",
    "constraints": [
        "17*x0 <= 454",
        "19*x0 <= 235",
        "16*x0 <= 527",
        "9*x0 <= 397",
        "16*x1 <= 454",
        "16*x1 <= 235",
        "7*x1 <= 527",
        "16*x1 <= 397",
        "15*x2 <= 454",
        "19*x2 <= 235",
        "8*x2 <= 527",
        "3*x2 <= 397",
        "2*x3 <= 454",
        "18*x3 <= 235",
        "6*x3 <= 527",
        "17*x3 <= 397",
        "17*x0 + 2*x3 >= 55",
        "16*x1 + 2*x3 >= 62",
        "19*x0 + 18*x3 >= 48",
        "19*x2 + 18*x3 >= 37",
        "19*x0 + 16*x1 + 18*x3 >= 41",
        "19*x0 + 16*x1 + 19*x2 >= 41",
        "9*x0 + 3*x2 >= 57",
        "9*x0 + 16*x1 >= 57",
        "16*x1 + 3*x2 >= 68",
        "3*x2 + 17*x3 >= 40",
        "6*x0 + 8*x2 - 4*x3 >= 0",
        "15*x2 + 2*x3 <= 360",
        "17*x0 + 16*x1 <= 446",
        "17*x0 + 2*x3 <= 335",
        "17*x0 + 15*x2 + 2*x3 <= 316",
        "17*x0 + 16*x1 + 15*x2 + 2*x3 <= 316",
        "19*x2 + 18*x3 <= 140",
        "19*x0 + 16*x1 <= 118",
        "16*x1 + 19*x2 <= 86",
        "19*x0 + 16*x1 + 19*x2 + 18*x3 <= 86",
        "16*x0 + 8*x2 <= 405",
        "7*x1 + 6*x3 <= 289",
        "7*x1 + 8*x2 <= 448",
        "16*x0 + 7*x1 <= 427",
        "16*x0 + 6*x3 <= 481",
        "16*x0 + 7*x1 + 8*x2 + 6*x3 <= 481",
        "9*x0 + 17*x3 <= 283",
        "16*x1 + 3*x2 <= 332",
        "9*x0 + 16*x1 <= 173",
        "16*x1 + 17*x3 <= 383",
        "16*x1 + 3*x2 + 17*x3 <= 361",
        "9*x0 + 16*x1 + 3*x2 + 17*x3 <= 361"
    ]
}
```