## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B2', 'milligrams of vitamin C', 'milligrams of vitamin B7']. Let's denote them as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to minimize is $9.96x_0 + 9.49x_1 + 2.05x_2$.

## Step 3: List all the constraints
The constraints are:
- $4x_0 \leq 92$
- $9x_0 \leq 134$
- $1x_0 \leq 135$
- $7x_0 \leq 63$
- $8x_1 \leq 92$
- $10x_1 \leq 134$
- $8x_1 \leq 135$
- $2x_1 \leq 63$
- $14x_2 \leq 92$
- $3x_2 \leq 134$
- $4x_2 \leq 135$
- $14x_2 \leq 63$
- $4x_0 + 8x_1 \geq 16$
- $4x_0 + 14x_2 \geq 28$
- $4x_0 + 8x_1 + 14x_2 \geq 28$
- $10x_1 + 3x_2 \geq 20$
- $9x_0 + 3x_2 \geq 31$
- $9x_0 + 10x_1 + 3x_2 \geq 31$
- $1x_0 + 4x_2 \geq 41$
- $1x_0 + 8x_1 \geq 45$
- $1x_0 + 8x_1 + 4x_2 \geq 24$
- $7x_0 + 2x_1 \geq 16$
- $7x_0 + 14x_2 \geq 16$
- $7x_0 + 2x_1 + 14x_2 \geq 16$
- $-6x_0 + 1x_1 \geq 0$
- $7x_1 - 10x_2 \geq 0$
- $1x_0 + 4x_2 \leq 117$
- $1x_0 + 8x_1 \leq 45$
- $7x_0 + 14x_2 \leq 25$

## 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 B2", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x1 = model.addVar(name="milligrams of vitamin C", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x2 = model.addVar(name="milligrams of vitamin B7", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

# Set the objective function
model.setObjective(9.96 * x0 + 9.49 * x1 + 2.05 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(4 * x0 <= 92)
model.addConstr(9 * x0 <= 134)
model.addConstr(1 * x0 <= 135)
model.addConstr(7 * x0 <= 63)

model.addConstr(8 * x1 <= 92)
model.addConstr(10 * x1 <= 134)
model.addConstr(8 * x1 <= 135)
model.addConstr(2 * x1 <= 63)

model.addConstr(14 * x2 <= 92)
model.addConstr(3 * x2 <= 134)
model.addConstr(4 * x2 <= 135)
model.addConstr(14 * x2 <= 63)

model.addConstr(4 * x0 + 8 * x1 >= 16)
model.addConstr(4 * x0 + 14 * x2 >= 28)
model.addConstr(4 * x0 + 8 * x1 + 14 * x2 >= 28)

model.addConstr(10 * x1 + 3 * x2 >= 20)
model.addConstr(9 * x0 + 3 * x2 >= 31)
model.addConstr(9 * x0 + 10 * x1 + 3 * x2 >= 31)

model.addConstr(1 * x0 + 4 * x2 >= 41)
model.addConstr(1 * x0 + 8 * x1 >= 45)
model.addConstr(1 * x0 + 8 * x1 + 4 * x2 >= 24)

model.addConstr(7 * x0 + 2 * x1 >= 16)
model.addConstr(7 * x0 + 14 * x2 >= 16)
model.addConstr(7 * x0 + 2 * x1 + 14 * x2 >= 16)

model.addConstr(-6 * x0 + 1 * x1 >= 0)
model.addConstr(7 * x1 - 10 * x2 >= 0)

model.addConstr(1 * x0 + 4 * x2 <= 117)
model.addConstr(1 * x0 + 8 * x1 <= 45)
model.addConstr(7 * x0 + 14 * x2 <= 25)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Milligrams of vitamin B2: ", x0.varValue)
    print("Milligrams of vitamin C: ", 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 B2"],
        ["x1", "milligrams of vitamin C"],
        ["x2", "milligrams of vitamin B7"]
    ],
    "objective_function": "9.96*x0 + 9.49*x1 + 2.05*x2",
    "constraints": [
        "4*x0 <= 92",
        "9*x0 <= 134",
        "1*x0 <= 135",
        "7*x0 <= 63",
        "8*x1 <= 92",
        "10*x1 <= 134",
        "8*x1 <= 135",
        "2*x1 <= 63",
        "14*x2 <= 92",
        "3*x2 <= 134",
        "4*x2 <= 135",
        "14*x2 <= 63",
        "4*x0 + 8*x1 >= 16",
        "4*x0 + 14*x2 >= 28",
        "4*x0 + 8*x1 + 14*x2 >= 28",
        "10*x1 + 3*x2 >= 20",
        "9*x0 + 3*x2 >= 31",
        "9*x0 + 10*x1 + 3*x2 >= 31",
        "1*x0 + 4*x2 >= 41",
        "1*x0 + 8*x1 >= 45",
        "1*x0 + 8*x1 + 4*x2 >= 24",
        "7*x0 + 2*x1 >= 16",
        "7*x0 + 14*x2 >= 16",
        "7*x0 + 2*x1 + 14*x2 >= 16",
        "-6*x0 + 1*x1 >= 0",
        "7*x1 - 10*x2 >= 0",
        "1*x0 + 4*x2 <= 117",
        "1*x0 + 8*x1 <= 45",
        "7*x0 + 14*x2 <= 25"
    ]
}
```