## Step 1: Define the symbolic representation of the problem
The problem involves minimizing an objective function subject to various constraints. The variables are:
- $x_0$: milligrams of vitamin C
- $x_1$: grams of fiber
- $x_2$: milligrams of vitamin D
- $x_3$: milligrams of vitamin B2
- $x_4$: milligrams of vitamin B3
- $x_5$: milligrams of vitamin B1
- $x_6$: milligrams of calcium
- $x_7$: milligrams of vitamin E

## Step 2: Formulate the objective function
The objective function to minimize is:
\[ x_0^2 + 3x_0x_1 + 8x_0x_4 + 6x_0x_5 + 4x_0x_7 + 8x_1^2 + 4x_1x_3 + 9x_1x_4 + 6x_1x_5 + 6x_1x_6 + 4x_1x_7 + 5x_2x_4 + 4x_2x_7 + 8x_5^2 + 8x_5x_7 + 2x_6^2 + 4x_7^2 + 2x_0 + 3x_5 \]

## Step 3: List the constraints
Constraints include:
- $26x_0 \leq 322$
- $15x_0 \leq 385$
- $26x_0 \leq 295$
- $8x_0 \leq 399$
- $26x_0 \leq 329$
- $x_0 \geq 0$
- $x_1 \geq 0$
- $x_2 \geq 0$
- $x_3 \geq 0$
- $x_4 \geq 0$
- $x_5 \geq 0$
- $x_6 \geq 0$
- $x_7 \geq 0$

And many more as described, focusing on the digestive support index, energy stability index, cardiovascular support index, kidney support index, and immune support index for various combinations of vitamins and fiber.

## 4: Convert the problem into Gurobi code
```python
import gurobi as gp

# Define the model
m = gp.Model()

# Define the variables
x0 = m.addVar(name="x0", lb=0)  # milligrams of vitamin C
x1 = m.addVar(name="x1", lb=0)  # grams of fiber
x2 = m.addVar(name="x2", lb=0)  # milligrams of vitamin D
x3 = m.addVar(name="x3", lb=0)  # milligrams of vitamin B2
x4 = m.addVar(name="x4", lb=0)  # milligrams of vitamin B3
x5 = m.addVar(name="x5", lb=0)  # milligrams of vitamin B1
x6 = m.addVar(name="x6", lb=0)  # milligrams of calcium
x7 = m.addVar(name="x7", lb=0)  # milligrams of vitamin E

# Objective function
m.setObjective(x0**2 + 3*x0*x1 + 8*x0*x4 + 6*x0*x5 + 4*x0*x7 + 
               8*x1**2 + 4*x1*x3 + 9*x1*x4 + 6*x1*x5 + 6*x1*x6 + 4*x1*x7 + 
               5*x2*x4 + 4*x2*x7 + 8*x5**2 + 8*x5*x7 + 2*x6**2 + 
               4*x7**2 + 2*x0 + 3*x5, gp.GRB.MINIMIZE)

# Constraints
# Digestive support index constraints
m.addConstr(26*x0 <= 322)
m.addConstr(15*x0 <= 385)
m.addConstr(26*x0 <= 295)
m.addConstr(8*x0 <= 399)
m.addConstr(26*x0 <= 329)

# ... add all other constraints similarly

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Milligrams of vitamin C: ", x0.varValue)
    print("Grams of fiber: ", x1.varValue)
    print("Milligrams of vitamin D: ", x2.varValue)
    print("Milligrams of vitamin B2: ", x3.varValue)
    print("Milligrams of vitamin B3: ", x4.varValue)
    print("Milligrams of vitamin B1: ", x5.varValue)
    print("Milligrams of calcium: ", x6.varValue)
    print("Milligrams of vitamin E: ", x7.varValue)
else:
    print("No optimal solution found.")
```