## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B3' and 'grams of fiber'. Let's denote 'milligrams of vitamin B3' as $x_1$ and 'grams of fiber' as $x_2$. The objective function to maximize is $2x_1 + 8x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
1. Immune support index of $x_1$ is 4: $4x_1$ (but this seems to be a given attribute, not a constraint)
2. Kidney support index of $x_1$ is 5: $5x_1$ (similarly, this is an attribute)
3. Immune support index of $x_2$ is 4: $4x_2$
4. Kidney support index of $x_2$ is 1: $1x_2$
5. Total combined immune support index $\geq 8$: $4x_1 + 4x_2 \geq 8$
6. Total combined kidney support index $\geq 4$: $5x_1 + 1x_2 \geq 4$
7. $4x_1 - 6x_2 \geq 0$
8. Total combined immune support index $\leq 17$: $4x_1 + 4x_2 \leq 17$
9. Total combined kidney support index $\leq 11$: $5x_1 + 1x_2 \leq 11$
10. $x_1$ must be a whole number (integer constraint)

## 3: Correct and Simplify Constraints
Correcting and simplifying the understanding of constraints:
- The attributes given for $x_0$ and $x_1$ seem to relate to $x_1$ and $x_2$ respectively, so we directly use them in constraints.
- The actual constraints from the problem statement are:
  - $4x_1 + 4x_2 \geq 8$
  - $5x_1 + x_2 \geq 4$
  - $4x_1 - 6x_2 \geq 0$
  - $4x_1 + 4x_2 \leq 17$
  - $5x_1 + x_2 \leq 11$
  - $x_1$ is an integer

## 4: Formulate the Symbolic Representation
The symbolic representation is:
- Variables: $x_1$ ('milligrams of vitamin B3'), $x_2$ ('grams of fiber')
- Objective function: Maximize $2x_1 + 8x_2$
- Constraints:
  1. $4x_1 + 4x_2 \geq 8$
  2. $5x_1 + x_2 \geq 4$
  3. $4x_1 - 6x_2 \geq 0$
  4. $4x_1 + 4x_2 \leq 17$
  5. $5x_1 + x_2 \leq 11$
  6. $x_1$ is an integer

## 5: Convert to Gurobi Code
```python
import gurobipy as gp

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

# Define variables
x1 = m.addVar(name="milligrams_of_vitamin_B3", vtype=gp.GRB.INTEGER)  # integer constraint
x2 = m.addVar(name="grams_of_fiber")

# Objective function: Maximize 2*x1 + 8*x2
m.setObjective(2*x1 + 8*x2, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(4*x1 + 4*x2 >= 8, name="immune_support_min")
m.addConstr(5*x1 + x2 >= 4, name="kidney_support_min")
m.addConstr(4*x1 - 6*x2 >= 0, name="linear_constraint_1")
m.addConstr(4*x1 + 4*x2 <= 17, name="immune_support_max")
m.addConstr(5*x1 + x2 <= 11, name="kidney_support_max")

# Solve the model
m.solve()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of vitamin B3: {x1.varValue}")
    print(f"Grams of fiber: {x2.varValue}")
    print(f"Objective: {m.objVal}")
else:
    print("No optimal solution found.")
```

## 6: Symbolic Representation Output
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B3'), ('x2', 'grams of fiber')],
    'objective_function': '2*x1 + 8*x2',
    'constraints': [
        '4*x1 + 4*x2 >= 8',
        '5*x1 + x2 >= 4',
        '4*x1 - 6*x2 >= 0',
        '4*x1 + 4*x2 <= 17',
        '5*x1 + x2 <= 11',
        'x1 is an integer'
    ]
}
```