## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B9', 'milligrams of magnesium', 'milligrams of vitamin B7']. Let's denote them as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Create a symbolic representation of the problem
The objective function to minimize is $7x_0 + 2x_1 + 2x_2$.

## Step 3: List all the constraints
The constraints given are:
- $11x_0 \geq 11$ (muscle growth index for $x_0$)
- $7x_0 \geq 7$ (immune support index for $x_0$)
- $4x_0 \geq 4$ (cognitive performance index for $x_0$)
- $3x_1 \geq 3$ (muscle growth index for $x_1$)
- $10x_1 \geq 10$ (immune support index for $x_1$)
- $2x_1 \geq 2$ (cognitive performance index for $x_1$)
- $11x_2 \geq 11$ (muscle growth index for $x_2$)
- $8x_2 \geq 8$ (immune support index for $x_2$)
- $5x_2 \geq 5$ (cognitive performance index for $x_2$)
- $11x_0 + 11x_2 \geq 23$ (total muscle growth index from $x_0$ and $x_2$)
- $11x_0 + 3x_1 \geq 8$ (total muscle growth index from $x_0$ and $x_1$)
- $11x_0 + 3x_1 + 11x_2 \geq 8$ (total muscle growth index from $x_0$, $x_1$, and $x_2$)
- $10x_1 + 8x_2 \geq 10$ (total immune support index from $x_1$ and $x_2$)
- $7x_0 + 10x_1 \geq 12$ (total immune support index from $x_0$ and $x_1$)
- $7x_0 + 10x_1 + 8x_2 \geq 17$ (total immune support index from $x_0$, $x_1$, and $x_2$)
- $7x_0 + 10x_1 + 8x_2 \geq 17$ (same as above, redundant)
- $4x_0 + 2x_1 \geq 27$ (total cognitive performance index from $x_0$ and $x_1$)
- $2x_1 + 5x_2 \geq 21$ (total cognitive performance index from $x_1$ and $x_2$)
- $4x_0 + 2x_1 + 5x_2 \geq 21$ (total cognitive performance index from $x_0$, $x_1$, and $x_2$)
- $5x_0 - 4x_2 \geq 0$
- $-10x_0 + 7x_1 \geq 0$
- $11x_0 + 3x_1 \leq 70$ (total muscle growth index from $x_0$ and $x_1$)
- $3x_1 + 11x_2 \leq 61$ (total muscle growth index from $x_1$ and $x_2$)

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

# Create a new model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name="milligrams_of_vitamin_B9", lb=0)
x1 = model.addVar(name="milligrams_of_magnesium", lb=0)
x2 = model.addVar(name="milligrams_of_vitamin_B7", lb=0)

# Define the objective function
model.setObjective(7 * x0 + 2 * x1 + 2 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(11 * x0 >= 11, name="muscle_growth_index_x0")
model.addConstr(7 * x0 >= 7, name="immune_support_index_x0")
model.addConstr(4 * x0 >= 4, name="cognitive_performance_index_x0")
model.addConstr(3 * x1 >= 3, name="muscle_growth_index_x1")
model.addConstr(10 * x1 >= 10, name="immune_support_index_x1")
model.addConstr(2 * x1 >= 2, name="cognitive_performance_index_x1")
model.addConstr(11 * x2 >= 11, name="muscle_growth_index_x2")
model.addConstr(8 * x2 >= 8, name="immune_support_index_x2")
model.addConstr(5 * x2 >= 5, name="cognitive_performance_index_x2")
model.addConstr(11 * x0 + 11 * x2 >= 23, name="muscle_growth_index_x0_x2")
model.addConstr(11 * x0 + 3 * x1 >= 8, name="muscle_growth_index_x0_x1")
model.addConstr(11 * x0 + 3 * x1 + 11 * x2 >= 8, name="muscle_growth_index_x0_x1_x2")
model.addConstr(10 * x1 + 8 * x2 >= 10, name="immune_support_index_x1_x2")
model.addConstr(7 * x0 + 10 * x1 >= 12, name="immune_support_index_x0_x1")
model.addConstr(7 * x0 + 10 * x1 + 8 * x2 >= 17, name="immune_support_index_x0_x1_x2")
model.addConstr(4 * x0 + 2 * x1 >= 27, name="cognitive_performance_index_x0_x1")
model.addConstr(2 * x1 + 5 * x2 >= 21, name="cognitive_performance_index_x1_x2")
model.addConstr(4 * x0 + 2 * x1 + 5 * x2 >= 21, name="cognitive_performance_index_x0_x1_x2")
model.addConstr(5 * x0 - 4 * x2 >= 0, name="vitamin_B9_B7_constraint")
model.addConstr(-10 * x0 + 7 * x1 >= 0, name="vitamin_B9_magnesium_constraint")
model.addConstr(11 * x0 + 3 * x1 <= 70, name="muscle_growth_index_x0_x1_ub")
model.addConstr(3 * x1 + 11 * x2 <= 61, name="muscle_growth_index_x1_x2_ub")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Milligrams of vitamin B9: ", x0.varValue)
    print("Milligrams of magnesium: ", x1.varValue)
    print("Milligrams of vitamin B7: ", x2.varValue)
    print("Objective function value: ", model.objVal)
else:
    print("No optimal solution found.")
```

## 5: Symbolic representation of the problem
```json
{
    "sym_variables": [
        ["x0", "milligrams of vitamin B9"],
        ["x1", "milligrams of magnesium"],
        ["x2", "milligrams of vitamin B7"]
    ],
    "objective_function": "7*x0 + 2*x1 + 2*x2",
    "constraints": [
        "11*x0 >= 11",
        "7*x0 >= 7",
        "4*x0 >= 4",
        "3*x1 >= 3",
        "10*x1 >= 10",
        "2*x1 >= 2",
        "11*x2 >= 11",
        "8*x2 >= 8",
        "5*x2 >= 5",
        "11*x0 + 11*x2 >= 23",
        "11*x0 + 3*x1 >= 8",
        "11*x0 + 3*x1 + 11*x2 >= 8",
        "10*x1 + 8*x2 >= 10",
        "7*x0 + 10*x1 >= 12",
        "7*x0 + 10*x1 + 8*x2 >= 17",
        "4*x0 + 2*x1 >= 27",
        "2*x1 + 5*x2 >= 21",
        "4*x0 + 2*x1 + 5*x2 >= 21",
        "5*x0 - 4*x2 >= 0",
        "-10*x0 + 7*x1 >= 0",
        "11*x0 + 3*x1 <= 70",
        "3*x1 + 11*x2 <= 61"
    ]
}
```