## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin K' and 'grams of fiber', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to minimize is $7x_1x_2 + 8x_2$.

## Step 3: List the constraints
The constraints given are:
- $10x_1 \leq 42$
- $5x_1 \leq 34$
- $7x_1 \leq 99$
- $17x_1 \leq 42$
- $14x_2 \leq 42$
- $15x_2 \leq 34$
- $2x_2 \leq 99$
- $8x_2 \leq 42$
- $10x_1 + 14x_2 \geq 12$
- $5x_1 + 15x_2 \geq 16$
- $7x_1^2 + 2x_2^2 \geq 30$
- $17x_1^2 + 8x_2^2 \geq 18$
- $-3x_1 + 5x_2 \geq 0$
- $10x_1^2 + 14x_2^2 \leq 31$
- $5x_1 + 15x_2 \leq 19$
- $7x_1^2 + 2x_2^2 \leq 76$
- $17x_1 + 8x_2 \leq 41$

## 4: Convert the problem into Gurobi code
We will use Gurobi to solve this optimization problem.

```python
import gurobi as gp

# Define the model
m = gp.Model()

# Define the variables
x1 = m.addVar(name="milligrams_of_vitamin_K", lb=0)  # Assuming non-negative
x2 = m.addVar(name="grams_of_fiber", lb=0)  # Assuming non-negative

# Define the objective function
m.setObjective(7 * x1 * x2 + 8 * x2, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(10 * x1 <= 42)
m.addConstr(5 * x1 <= 34)
m.addConstr(7 * x1 <= 99)
m.addConstr(17 * x1 <= 42)
m.addConstr(14 * x2 <= 42)
m.addConstr(15 * x2 <= 34)
m.addConstr(2 * x2 <= 99)
m.addConstr(8 * x2 <= 42)
m.addConstr(10 * x1 + 14 * x2 >= 12)
m.addConstr(5 * x1 + 15 * x2 >= 16)
m.addConstr(7 * x1**2 + 2 * x2**2 >= 30)
m.addConstr(17 * x1**2 + 8 * x2**2 >= 18)
m.addConstr(-3 * x1 + 5 * x2 >= 0)
m.addConstr(10 * x1**2 + 14 * x2**2 <= 31)
m.addConstr(5 * x1 + 15 * x2 <= 19)
m.addConstr(7 * x1**2 + 2 * x2**2 <= 76)
m.addConstr(17 * x1 + 8 * x2 <= 41)

# Solve the model
m.optimize()

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

## 5: Symbolic representation of the problem
The symbolic representation is as follows:
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin K'), ('x2', 'grams of fiber')],
    'objective_function': '7*x1*x2 + 8*x2',
    'constraints': [
        '10*x1 <= 42',
        '5*x1 <= 34',
        '7*x1 <= 99',
        '17*x1 <= 42',
        '14*x2 <= 42',
        '15*x2 <= 34',
        '2*x2 <= 99',
        '8*x2 <= 42',
        '10*x1 + 14*x2 >= 12',
        '5*x1 + 15*x2 >= 16',
        '7*x1^2 + 2*x2^2 >= 30',
        '17*x1^2 + 8*x2^2 >= 18',
        '-3*x1 + 5*x2 >= 0',
        '10*x1^2 + 14*x2^2 <= 31',
        '5*x1 + 15*x2 <= 19',
        '7*x1^2 + 2*x2^2 <= 76',
        '17*x1 + 8*x2 <= 41'
    ]
}
```