## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B4' and 'milligrams of magnesium'. Let's denote 'milligrams of vitamin B4' as $x_1$ and 'milligrams of magnesium' as $x_2$. The objective function to maximize is $1x_1^2 + 9x_1x_2 + 2x_2^2 + 2x_2$.

## Step 2: Translate the given constraints into symbolic notation
The constraints given are:
- The kidney support index of $x_1$ is 1, so $1x_1 = 1$.
- The immune support index of $x_1$ is 8, so $8x_1 = 8$.
- The kidney support index of $x_2$ is 4, so $4x_2$ has no direct constraint but is part of $r0$.
- The immune support index of $x_2$ is 3, so $3x_2$ has no direct constraint but is part of $r1$.
- The total combined kidney support index from $x_1$ and $x_2$ squared should be greater than or equal to 15: $1^2x_1^2 + 4^2x_2^2 \geq 15$ or $x_1^2 + 16x_2^2 \geq 15$.
- The total combined immune support index from $x_1$ and $x_2$ has to be greater than or equal to 5: $8x_1 + 3x_2 \geq 5$.
- $7x_1^2 - 3x_2^2 \geq 0$.
- The total combined kidney support index from $x_1$ and $x_2$ must be less than or equal to 35: $1x_1 + 4x_2 \leq 35$.
- The total combined immune support index from $x_1$ and $x_2$ must be 17 at a maximum: $8x_1 + 3x_2 \leq 17$.
- The total combined immune support index from $x_1^2$ and $x_2^2$ has to be 17 at a maximum: $64x_1^2 + 9x_2^2 \leq 17$.

## 3: Correct the interpretation of constraints
Upon closer inspection, it seems there was an initial misinterpretation of how the constraints are directly provided:
- $x_1 = 1$ (from $1x_1 = 1$),
- $8x_1 = 8$ implies $x_1 = 1$,
- Direct constraints from $r0$ and $r1$ are: $x_1 + 4x_2 \leq 53$ and $8x_1 + 3x_2 \leq 21$.

## 4: Formulate the symbolic representation
Given:
- Variables: $x_1$ ('milligrams of vitamin B4'), $x_2$ ('milligrams of magnesium'),
- Objective function: $x_1^2 + 9x_1x_2 + 2x_2^2 + 2x_2$,
- Constraints:
1. $x_1 = 1$,
2. $x_1^2 + 16x_2^2 \geq 15$,
3. $8x_1 + 3x_2 \geq 5$,
4. $7x_1^2 - 3x_2^2 \geq 0$,
5. $x_1 + 4x_2 \leq 35$,
6. $8x_1 + 3x_2 \leq 17$,
7. $64x_1^2 + 9x_2^2 \leq 17$.

## 5: Convert to Gurobi code
```python
import gurobi

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

# Define variables
x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x1")  # milligrams of vitamin B4
x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x2")  # milligrams of magnesium

# Objective function
model.setObjective(x1**2 + 9*x1*x2 + 2*x2**2 + 2*x2, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 == 1, name="c1")
model.addConstr(x1**2 + 16*x2**2 >= 15, name="c2")
model.addConstr(8*x1 + 3*x2 >= 5, name="c3")
model.addConstr(7*x1**2 - 3*x2**2 >= 0, name="c4")
model.addConstr(x1 + 4*x2 <= 35, name="c5")
model.addConstr(8*x1 + 3*x2 <= 17, name="c6")
model.addConstr(64*x1**2 + 9*x2**2 <= 17, name="c7")

# Optimize
model.optimize()

# Print solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"x1: {x1.varValue}")
    print(f"x2: {x2.varValue}")
    print(f"Objective: {model.objVal}")
else:
    print("No optimal solution found.")
```

## 6: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B4'), ('x2', 'milligrams of magnesium')],
    'objective_function': 'x1^2 + 9*x1*x2 + 2*x2^2 + 2*x2',
    'constraints': [
        'x1 = 1',
        'x1^2 + 16*x2^2 >= 15',
        '8*x1 + 3*x2 >= 5',
        '7*x1^2 - 3*x2^2 >= 0',
        'x1 + 4*x2 <= 35',
        '8*x1 + 3*x2 <= 17',
        '64*x1^2 + 9*x2^2 <= 17'
    ]
}
```