## Step 1: Define the symbolic representation of the problem
The symbolic representation of the problem consists of pairs of variables in symbolic notation and their corresponding natural language objects, the objective function rendered as an algebraic term, and the list of semi-algebraic constraints.

## Step 2: Identify the variables and their corresponding symbolic notation
Let's denote the variables as follows:
- $x_0$ for 'grams of carbohydrates'
- $x_1$ for 'milligrams of vitamin K'
- $x_2$ for 'milligrams of iron'
- $x_3$ for 'milligrams of vitamin B12'
- $x_4$ for 'milligrams of vitamin B1'
- $x_5$ for 'milligrams of vitamin B7'
- $x_6$ for 'milligrams of vitamin B9'
- $x_7$ for 'milligrams of vitamin C'

## Step 3: Formulate the objective function
The objective function to minimize is:
$1.09x_0 + 1.84x_1 + 9.7x_2 + 5.2x_3 + 3.89x_4 + 1.97x_5 + 4.5x_6 + 2.81x_7$

## Step 4: List the constraints
The constraints are:
- $5.02x_0 + 24.9 \leq 398$ (cognitive performance index for carbohydrates)
- $23.04x_1 + 24.07 \leq 398$ (cognitive performance index for vitamin K)
- $16.85x_2 + 19.5 \leq 398$ (cognitive performance index for iron)
- $19.3x_3 + 25.4 \leq 398$ (cognitive performance index for vitamin B12)
- $6.39x_4 + 19.78 \leq 398$ (cognitive performance index for vitamin B1)
- $22.9x_5 + 13.24 \leq 398$ (cognitive performance index for vitamin B7)
- $24.37x_6 + 5.6 \leq 398$ (cognitive performance index for vitamin B9)
- $21.59x_7 + 0.02 \leq 398$ (cognitive performance index for vitamin C)
- $5.02x_0 + 24.9 \leq 526$ (digestive support index for carbohydrates)
- $23.04x_1 + 24.07 \leq 526$ (digestive support index for vitamin K)
- $16.85x_2 + 19.5 \leq 526$ (digestive support index for iron)
- $19.3x_3 + 25.4 \leq 526$ (digestive support index for vitamin B12)
- $6.39x_4 + 19.78 \leq 526$ (digestive support index for vitamin B1)
- $22.9x_5 + 13.24 \leq 526$ (digestive support index for vitamin B7)
- $24.37x_6 + 5.6 \leq 526$ (digestive support index for vitamin B9)
- $21.59x_7 + 0.02 \leq 526$ (digestive support index for vitamin C)
- And many more constraints as described in the problem...

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

# Define the model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="x0", vtype=gp.GRB.INTEGER)  # grams of carbohydrates
x1 = m.addVar(name="x1")  # milligrams of vitamin K
x2 = m.addVar(name="x2")  # milligrams of iron
x3 = m.addVar(name="x3")  # milligrams of vitamin B12
x4 = m.addVar(name="x4", vtype=gp.GRB.INTEGER)  # milligrams of vitamin B1
x5 = m.addVar(name="x5", vtype=gp.GRB.INTEGER)  # milligrams of vitamin B7
x6 = m.addVar(name="x6")  # milligrams of vitamin B9
x7 = m.addVar(name="x7")  # milligrams of vitamin C

# Define the objective function
m.setObjective(1.09*x0 + 1.84*x1 + 9.7*x2 + 5.2*x3 + 3.89*x4 + 1.97*x5 + 4.5*x6 + 2.81*x7, gp.GRB.MINIMIZE)

# Add constraints
# Cognitive performance index constraints
m.addConstr(5.02*x0 + 23.04*x1 + 16.85*x2 + 19.3*x3 + 6.39*x4 + 22.9*x5 + 24.37*x6 + 21.59*x7 <= 398)

# Digestive support index constraints
m.addConstr(24.9*x0 + 24.07*x1 + 19.5*x2 + 25.4*x3 + 19.78*x4 + 13.24*x5 + 5.6*x6 + 0.02*x7 <= 526)

# Add more constraints here...

# Optimize the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("x0: ", x0.varValue)
    print("x1: ", x1.varValue)
    print("x2: ", x2.varValue)
    print("x3: ", x3.varValue)
    print("x4: ", x4.varValue)
    print("x5: ", x5.varValue)
    print("x6: ", x6.varValue)
    print("x7: ", x7.varValue)
else:
    print("No optimal solution found")
```