To solve this optimization problem, we first need to define the variables and the objective function, and then add all the constraints.

## Step 1: Define the variables
Let's denote the variables as follows:
- $x_0$ = milligrams of calcium
- $x_1$ = milligrams of vitamin B3
- $x_2$ = grams of fiber
- $x_3$ = milligrams of vitamin D
- $x_4$ = milligrams of vitamin E
- $x_5$ = milligrams of vitamin A

## Step 2: Define the objective function
The objective function to maximize is:
\[ 9x_0^2 + 3x_0x_1 + 7x_0x_2 + x_0x_3 + 5x_1^2 + 6x_1x_2 + 6x_1x_4 + 8x_2^2 + x_2x_3 + 7x_2x_4 + x_2x_5 + 6x_3^2 + x_3x_4 + 4x_3x_5 + 2x_4x_5 + 3x_5^2 + 5x_1 + 2x_2 + 5x_3 + 3x_4 \]

## Step 3: Define the constraints
The constraints are given based on the resource/attribute indices. 

## Step 4: Implement the problem in Gurobi
We will use Gurobi's Python API to model and solve this problem.

```python
import gurobi as gp

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

# Define the variables
x0 = m.addVar(name="milligrams_of_calcium", vtype=gp.GRB.INTEGER)  # Cannot be fractional
x1 = m.addVar(name="milligrams_of_vitamin_B3", vtype=gp.GRB.INTEGER)  # Cannot be fractional
x2 = m.addVar(name="grams_of_fiber")  # Can be fractional
x3 = m.addVar(name="milligrams_of_vitamin_D", vtype=gp.GRB.INTEGER)  # Must be non-fractional
x4 = m.addVar(name="milligrams_of_vitamin_E", vtype=gp.GRB.INTEGER)  # Must be a whole number
x5 = m.addVar(name="milligrams_of_vitamin_A")  # Can be non-whole number

# Objective function
obj = 9*x0**2 + 3*x0*x1 + 7*x0*x2 + x0*x3 + 5*x1**2 + 6*x1*x2 + 6*x1*x4 + 8*x2**2 + x2*x3 + 7*x2*x4 + x2*x5 + 6*x3**2 + x3*x4 + 4*x3*x5 + 2*x4*x5 + 3*x5**2 + 5*x1 + 2*x2 + 5*x3 + 3*x4
m.setObjective(obj, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(28*x0 + 22*x2 >= 59, name="energy_stability_index_constraint1")
m.addConstr(13*x1**2 + 20*x4**2 >= 56, name="energy_stability_index_constraint2")
m.addConstr(x0**2 + x2**2 + x5**2 >= 56, name="energy_stability_index_constraint3")
m.addConstr(28*x0 + 13*x1 + 20*x4 >= 70, name="energy_stability_index_constraint4")
m.addConstr(28*x0 + 22*x2 + 31*x5 >= 70, name="energy_stability_index_constraint5")
m.addConstr(3*x2 + 11*x5 >= 31, name="immune_support_index_constraint1")
m.addConstr(23*x0**2 + 29*x4**2 >= 45, name="immune_support_index_constraint2")
m.addConstr(16*x1 + 29*x4 >= 31, name="immune_support_index_constraint3")
m.addConstr(16*x1 + 3*x2 >= 83, name="immune_support_index_constraint4")
m.addConstr(16*x1**2 + 11*x5**2 >= 50, name="immune_support_index_constraint5")
m.addConstr(17*x1**2 + 18*x2**2 + 22*x3**2 >= 48, name="cardiovascular_support_index_constraint1")

# ... Add all other constraints similarly

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of calcium: {x0.varValue}")
    print(f"Milligrams of vitamin B3: {x1.varValue}")
    print(f"Grams of fiber: {x2.varValue}")
    print(f"Milligrams of vitamin D: {x3.varValue}")
    print(f"Milligrams of vitamin E: {x4.varValue}")
    print(f"Milligrams of vitamin A: {x5.varValue}")
else:
    print("No optimal solution found.")
```