## Step 1: Define the symbolic representation of the problem
The problem involves three variables: 'milligrams of vitamin B6', 'milligrams of vitamin B2', and 'milligrams of zinc'. Let's denote these as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $1 \cdot x_0^2 + 9 \cdot x_0 \cdot x_1 + 7 \cdot x_0 \cdot x_2 + 1 \cdot x_1^2 + 9 \cdot x_2^2 + 2 \cdot x_0 + 4 \cdot x_1 + 9 \cdot x_2$.

## 3: Define the constraints in symbolic notation
The constraints are:
- $x_0 \geq 0$ and $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity, but not explicitly stated, so we assume they can be any real number as per the problem statement)
- $9x_0 = 9$ (immune support index of $x_0$)
- $23x_0 = 23$ (muscle growth index of $x_0$)
- $16x_0 = 16$ (kidney support index of $x_0$)
- $22x_1 = 22$ (immune support index of $x_1$)
- $9x_1 = 9$ (muscle growth index of $x_1$)
- $1x_1 = 1$ (kidney support index of $x_1$)
- $4x_2 = 4$ (immune support index of $x_2$)
- $23x_2 = 23$ (muscle growth index of $x_2$)
- $17x_2 = 17$ (kidney support index of $x_2$)
- $22x_1 + 4x_2 \geq 28$ (total immune support index from $x_1$ and $x_2$)
- $9x_0 + 4x_2 \geq 15$ (total immune support index from $x_0$ and $x_2$)
- $9x_0 + 22x_1 + 4x_2 \geq 15$ (total immune support index from $x_0$, $x_1$, and $x_2$)
- $9x_1 + 23x_2 \geq 15$ (total muscle growth index from $x_1$ and $x_2$)
- $23x_0 + 23x_2 \geq 30$ (total muscle growth index from $x_0$ and $x_2$)
- $(23x_0)^2 + (9x_1)^2 + (23x_2)^2 \geq 21$ (total muscle growth index from $x_0$, $x_1$, and $x_2$ squared)
- $23x_0 + 9x_1 + 23x_2 \geq 21$ (total muscle growth index from $x_0$, $x_1$, and $x_2$)
- $16x_0 + 17x_2 \geq 23$ (total kidney support index from $x_0$ and $x_2$)
- $1x_1 + 17x_2 \geq 28$ (total kidney support index from $x_1$ and $x_2$)
- $16x_0 + 1x_1 + 17x_2 \geq 28$ (total kidney support index from $x_0$, $x_1$, and $x_2$)
- $3x_1 - 5x_2 \geq 0$
- $9x_0 + 22x_1 + 4x_2 \leq 124$ (total immune support index from $x_0$, $x_1$, and $x_2$)
- $16x_0 + 1x_1 \leq 71$ (total kidney support index from $x_0$ and $x_1$)
- $16x_0 + 17x_2 \leq 86$ (total kidney support index from $x_0$ and $x_2$)
- $1x_1 + 17x_2 \leq 97$ (total kidney support index from $x_1$ and $x_2$)

However, upon closer inspection, we see that some of the constraints directly set the values of $x_0$, $x_1$, and $x_2$:
- $9x_0 = 9 \Rightarrow x_0 = 1$
- $23x_0 = 23 \Rightarrow x_0 = 1$
- $16x_0 = 16 \Rightarrow x_0 = 1$
- $22x_1 = 22 \Rightarrow x_1 = 1$
- $9x_1 = 9 \Rightarrow x_1 = 1$
- $1x_1 = 1 \Rightarrow x_1 = 1$
- $4x_2 = 4 \Rightarrow x_2 = 1$
- $23x_2 = 23 \Rightarrow x_2 = 1$
- $17x_2 = 17 \Rightarrow x_2 = 1$

This implies $x_0 = 1$, $x_1 = 1$, and $x_2 = 1$.

## 4: Verify if the given solution satisfies all constraints
Given $x_0 = 1$, $x_1 = 1$, and $x_2 = 1$, let's verify if these values satisfy all the constraints and calculate the objective function.

## 5: Calculate the objective function with the given values
The objective function becomes:
$1 \cdot 1^2 + 9 \cdot 1 \cdot 1 + 7 \cdot 1 \cdot 1 + 1 \cdot 1^2 + 9 \cdot 1^2 + 2 \cdot 1 + 4 \cdot 1 + 9 \cdot 1 = 1 + 9 + 7 + 1 + 9 + 2 + 4 + 9 = 42$.

## 6: Implement the problem in Gurobi
Given that we have found a potential solution that satisfies the equality constraints directly, we should verify if this problem can be directly solved or if it's infeasible due to other constraints.

```python
import gurobi as gp

# Define the model
m = gp.Model()

# Define the variables
x0 = m.addVar(lb=-gp.GRB.INFINITY, name="x0")  # milligrams of vitamin B6
x1 = m.addVar(lb=-gp.GRB.INFINITY, name="x1")  # milligrams of vitamin B2
x2 = m.addVar(lb=-gp.GRB.INFINITY, name="x2")  # milligrams of zinc

# Objective function
m.setObjective(x0**2 + 9*x0*x1 + 7*x0*x2 + x1**2 + 9*x2**2 + 2*x0 + 4*x1 + 9*x2)

# Constraints
m.addConstr(9*x0 == 9)
m.addConstr(23*x0 == 23)
m.addConstr(16*x0 == 16)
m.addConstr(22*x1 == 22)
m.addConstr(9*x1 == 9)
m.addConstr(x1 == 1)
m.addConstr(4*x2 == 4)
m.addConstr(23*x2 == 23)
m.addConstr(17*x2 == 17)
m.addConstr(22*x1 + 4*x2 >= 28)
m.addConstr(9*x0 + 4*x2 >= 15)
m.addConstr(9*x0 + 22*x1 + 4*x2 >= 15)
m.addConstr(9*x1 + 23*x2 >= 15)
m.addConstr(23*x0 + 23*x2 >= 30)
m.addConstr((23*x0)**2 + (9*x1)**2 + (23*x2)**2 >= 21)
m.addConstr(23*x0 + 9*x1 + 23*x2 >= 21)
m.addConstr(16*x0 + 17*x2 >= 23)
m.addConstr(x1 + 17*x2 >= 28)
m.addConstr(16*x0 + x1 + 17*x2 >= 28)
m.addConstr(3*x1 - 5*x2 >= 0)
m.addConstr(9*x0 + 22*x1 + 4*x2 <= 124)
m.addConstr(16*x0 + x1 <= 71)
m.addConstr(16*x0 + 17*x2 <= 86)
m.addConstr(x1 + 17*x2 <= 97)

# Solve the model
m.optimize()

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

## 7: Symbolic Representation
```json
{
    'sym_variables': [('x0', 'milligrams of vitamin B6'), ('x1', 'milligrams of vitamin B2'), ('x2', 'milligrams of zinc')],
    'objective_function': 'x0^2 + 9*x0*x1 + 7*x0*x2 + x1^2 + 9*x2^2 + 2*x0 + 4*x1 + 9*x2',
    'constraints': [
        '9*x0 = 9',
        '23*x0 = 23',
        '16*x0 = 16',
        '22*x1 = 22',
        '9*x1 = 9',
        'x1 = 1',
        '4*x2 = 4',
        '23*x2 = 23',
        '17*x2 = 17',
        '22*x1 + 4*x2 >= 28',
        '9*x0 + 4*x2 >= 15',
        '9*x0 + 22*x1 + 4*x2 >= 15',
        '9*x1 + 23*x2 >= 15',
        '23*x0 + 23*x2 >= 30',
        '(23*x0)^2 + (9*x1)^2 + (23*x2)^2 >= 21',
        '23*x0 + 9*x1 + 23*x2 >= 21',
        '16*x0 + 17*x2 >= 23',
        'x1 + 17*x2 >= 28',
        '16*x0 + x1 + 17*x2 >= 28',
        '3*x1 - 5*x2 >= 0',
        '9*x0 + 22*x1 + 4*x2 <= 124',
        '16*x0 + x1 <= 71',
        '16*x0 + 17*x2 <= 86',
        'x1 + 17*x2 <= 97'
    ]
}
```