## Step 1: Define the symbolic representation of the variables
The variables are 'grams of fat', 'milligrams of vitamin B3', and 'grams of fiber', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $6x_1^2 + 3x_1x_2 + 8x_1x_3 + 4x_2^2 + 3x_2x_3 + 9x_3^2 + 8x_1 + 8x_2 + 8x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $2x_1 \leq 74$
- $5x_1 \leq 39$
- $8x_2 \leq 74$
- $4x_2 \leq 39$
- $8x_3 \leq 74$
- $x_3 \leq 39$ is not given, instead $x_2$ and $x_3$ have an upper bound from $r_0$ and $r_1$ but we have $8x_3$ for $r_0$ and $1x_3$ for $r_1$
- $8x_2 + 8x_3 \geq 14$
- $2x_1 + 8x_2 + 8x_3 \geq 14$
- $4x_2^2 + x_3^2 \geq 5$
- $5x_1^2 + x_3^2 \geq 7$
- $5x_1 + 4x_2 + x_3 \geq 7$
- $7x_1 - 5x_3 \geq 0$

## 4: Correcting and Compiling the Constraints
Correcting the interpretation of given resources/attributes:
- The actual constraints from resources are:
  - $r_0: 2x_1 + 8x_2 + 8x_3 \leq 74$
  - $r_1: 5x_1 + 4x_2 + x_3 \leq 39$

## 5: Symbolic Representation
The symbolic representation of the problem is:
- Variables: $x_1$ ('grams of fat'), $x_2$ ('milligrams of vitamin B3'), $x_3$ ('grams of fiber')
- Objective Function: $6x_1^2 + 3x_1x_2 + 8x_1x_3 + 4x_2^2 + 3x_2x_3 + 9x_3^2 + 8x_1 + 8x_2 + 8x_3$
- Constraints:
  1. $2x_1 \leq 74$
  2. $5x_1 \leq 39$
  3. $8x_2 \leq 74$
  4. $4x_2 \leq 39$
  5. $8x_3 \leq 74$
  6. $x_3 \leq 39$ 
  7. $8x_2 + 8x_3 \geq 14$
  8. $2x_1 + 8x_2 + 8x_3 \geq 14$
  9. $4x_2^2 + x_3^2 \geq 5$
  10. $5x_1^2 + x_3^2 \geq 7$
  11. $5x_1 + 4x_2 + x_3 \geq 7$
  12. $7x_1 - 5x_3 \geq 0$

## 6: Gurobi Code
```python
import gurobi

# Create a new model
m = gurobi.Model()

# Define the variables
x1 = m.addVar(name="x1", lb=0)  # grams of fat
x2 = m.addVar(name="x2", lb=0)  # milligrams of vitamin B3
x3 = m.addVar(name="x3", lb=0)  # grams of fiber

# Objective function
m.setObjective(6*x1**2 + 3*x1*x2 + 8*x1*x3 + 4*x2**2 + 3*x2*x3 + 9*x3**2 + 8*x1 + 8*x2 + 8*x3, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(2*x1 <= 74)
m.addConstr(5*x1 <= 39)
m.addConstr(8*x2 <= 74)
m.addConstr(4*x2 <= 39)
m.addConstr(8*x3 <= 74)
m.addConstr(8*x2 + 8*x3 >= 14)
m.addConstr(2*x1 + 8*x2 + 8*x3 >= 14)
m.addConstr(4*x2**2 + x3**2 >= 5)
m.addConstr(5*x1**2 + x3**2 >= 7)
m.addConstr(5*x1 + 4*x2 + x3 >= 7)
m.addConstr(7*x1 - 5*x3 >= 0)

# Update model
m.update()

# Solve the model
m.optimize()

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

## 7: Final Output
```json
{
    'sym_variables': [('x1', 'grams of fat'), ('x2', 'milligrams of vitamin B3'), ('x3', 'grams of fiber')],
    'objective_function': '6*x1^2 + 3*x1*x2 + 8*x1*x3 + 4*x2^2 + 3*x2*x3 + 9*x3^2 + 8*x1 + 8*x2 + 8*x3',
    'constraints': [
        '2*x1 <= 74',
        '5*x1 <= 39',
        '8*x2 <= 74',
        '4*x2 <= 39',
        '8*x3 <= 74',
        '8*x2 + 8*x3 >= 14',
        '2*x1 + 8*x2 + 8*x3 >= 14',
        '4*x2^2 + x3^2 >= 5',
        '5*x1^2 + x3^2 >= 7',
        '5*x1 + 4*x2 + x3 >= 7',
        '7*x1 - 5*x3 >= 0'
    ]
}
```