## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B2' and 'milligrams of magnesium'. Let's denote 'milligrams of vitamin B2' as $x_1$ and 'milligrams of magnesium' as $x_2$. The objective function to minimize is $9x_1^2 + 4x_1x_2 + 7x_2^2 + 8x_1$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- The immune support index for $x_1$ is 3, so $3x_1$,
- The digestive support index for $x_1$ is 8, so $8x_1$,
- The immune support index for $x_2$ is 2, so $2x_2$,
- The digestive support index for $x_2$ is 1, so $1x_2$,
- The total combined immune support index is at least 55: $3x_1 + 2x_2 \geq 55$,
- The total combined digestive support index is at least 21: $8x_1 + 1x_2 \geq 21$ and $8x_1^2 + 1x_2^2 \geq 21$,
- $-3x_1 + 3x_2 \geq 0$,
- The total combined immune support index is at most 142: $3x_1 + 2x_2 \leq 142$,
- The total combined digestive support index is at most 64: $8x_1^2 + 1x_2^2 \leq 64$.

## 3: Correct and consolidate the constraints
Correcting and consolidating the constraints:
- $3x_1 = 3$ (equality constraint, not inequality),
- $8x_1 = 8$ (equality constraint, not inequality),
- $2x_2$ and $1x_2$ are just attributes and not constraints by themselves,
- $3x_1 + 2x_2 \geq 55$,
- $8x_1 + x_2 \geq 21$,
- $8x_1^2 + x_2^2 \geq 21$,
- $-3x_1 + 3x_2 \geq 0$,
- $3x_1 + 2x_2 \leq 142$,
- $8x_1^2 + x_2^2 \leq 64$.

## 4: Equality constraints
From $3x_1 = 3$, we get $x_1 = 1$.
From $8x_1 = 8$, we get $x_1 = 1$.
Both confirm $x_1 = 1$.

## 5: Formulate the problem in Gurobi
Given $x_1 = 1$, we substitute it into the constraints and objective function:
- Objective function: $9(1)^2 + 4(1)x_2 + 7x_2^2 + 8(1) = 9 + 4x_2 + 7x_2^2 + 8 = 7x_2^2 + 4x_2 + 17$,
- Constraints:
  - $3(1) + 2x_2 \geq 55$ => $2x_2 \geq 52$ => $x_2 \geq 26$,
  - $8(1) + x_2 \geq 21$ => $x_2 \geq 13$,
  - $8(1)^2 + x_2^2 \geq 21$ => $64 + x_2^2 \geq 21$ => $x_2^2 \geq -43$ (always true since $x_2^2 \geq 0$),
  - $-3(1) + 3x_2 \geq 0$ => $3x_2 \geq 3$ => $x_2 \geq 1$,
  - $3(1) + 2x_2 \leq 142$ => $2x_2 \leq 139$ => $x_2 \leq 69.5$,
  - $8(1)^2 + x_2^2 \leq 64$ => $64 + x_2^2 \leq 64$ => $x_2^2 \leq 0$ => $x_2 = 0$.

## 6: Solve for $x_2$
Given $x_2 = 0$ from the last constraint, it seems there might be an inconsistency because $x_2$ cannot simultaneously satisfy $x_2 \geq 26$ and $x_2 = 0$. This indicates the problem might be infeasible.

## 7: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B2'), ('x2', 'milligrams of magnesium')],
    'objective_function': '9*x1^2 + 4*x1*x2 + 7*x2^2 + 8*x1',
    'constraints': [
        '3*x1 = 3',
        '8*x1 = 8',
        '3*x1 + 2*x2 >= 55',
        '8*x1 + x2 >= 21',
        '8*x1^2 + x2^2 >= 21',
        '-3*x1 + 3*x2 >= 0',
        '3*x1 + 2*x2 <= 142',
        '8*x1^2 + x2^2 <= 64'
    ]
}
```

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

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x1")
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x2")
    
    # Objective function
    model.setObjective(9*x1**2 + 4*x1*x2 + 7*x2**2 + 8*x1, gurobi.GRB.MINIMIZE)
    
    # Constraints
    model.addConstr(3*x1 == 3, name="immune_support_index_B2")
    model.addConstr(8*x1 == 8, name="digestive_support_index_B2")
    model.addConstr(3*x1 + 2*x2 >= 55, name="min_immune_support")
    model.addConstr(8*x1 + x2 >= 21, name="min_digestive_support")
    model.addConstr(8*x1**2 + x2**2 >= 21, name="min_digestive_support_squared")
    model.addConstr(-3*x1 + 3*x2 >= 0, name="balance_constraint")
    model.addConstr(3*x1 + 2*x2 <= 142, name="max_immune_support")
    model.addConstr(8*x1**2 + x2**2 <= 64, name="max_digestive_support_squared")
    
    # Solve the model
    model.optimize()
    
    # Print the status
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print("x1:", x1.varValue)
        print("x2:", x2.varValue)
        print("Objective:", model.objVal)
    elif model.status == gurobi.GRB.Status.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

solve_optimization_problem()
```