To solve the given optimization problem using Gurobi, we first need to define the decision variables and the objective function, followed by the constraints as described in the problem statement. The decision variables are 'milligrams of vitamin B2' (x0) and 'milligrams of magnesium' (x1). 

The objective function to minimize is: 9*x0^2 + 4*x0*x1 + 7*x1^2 + 8*x0.

The constraints given can be translated into the following:

1. Immune support index for milligrams of vitamin B2 is 3.
2. Digestive support index for milligrams of vitamin B2 is 8.
3. Immune support index for milligrams of magnesium is 2.
4. Digestive support index for milligrams of magnesium is 1.
5. The total combined immune support index from both must be at least 55.
6. The total combined digestive support index (squared terms) must be at least 21.
7. -3*x0 + 3*x1 >= 0.
8. The total combined immune support index should not exceed 142.
9. The total combined digestive support index (squared terms) should not exceed 64.

We also need to consider the upper bounds of the immune and digestive support indices as given in the resources/attributes: r0 ('immune support index') has an upper bound of 148, and r1 ('digestive support index') has an upper bound of 76. However, these seem more like descriptions rather than direct constraints on x0 and x1.

Let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define the decision variables
x0 = m.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name='milligrams_of_vitamin_B2')
x1 = m.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name='milligrams_of_magnesium')

# Objective function
m.setObjective(9*x0**2 + 4*x0*x1 + 7*x1**2 + 8*x0, GRB.MINIMIZE)

# Constraints
# Since constraints 1-4 describe the indices per unit of x0 and x1, we'll directly apply them in combined constraints.
m.addConstr(3*x0 + 2*x1 >= 55)  # Combined immune support index at least 55
m.addConstr((8*x0)**2 + (x1)**2 >= 21)  # This seems to be a misunderstanding; the original problem mentions digestive support indices, not squared terms directly. Adjusted for clarity.
m.addConstr(-3*x0 + 3*x1 >= 0)
m.addConstr(3*x0 + 2*x1 <= 142)  # Combined immune support index at most 142
m.addConstr((8*x0)**2 + (x1)**2 <= 64)  # Again, adjusted for the squared terms.

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print('Optimal solution found:')
    print(f'milligrams_of_vitamin_B2: {x0.x}')
    print(f'milligrams_of_magnesium: {x1.x}')
else:
    print('No optimal solution found')
```