## Step 1: Define the symbolic representation of the problem

Let's define the symbolic variables:
- $x_1$ = glasses of orange juice
- $x_2$ = glasses of apple juice

The objective is to minimize the amount of Glycine, which is $7x_1 + x_2$.

## Step 2: Translate the constraints into symbolic notation

The constraints given are:
- At least 30 units of Lysine: $2x_1 + 5x_2 \geq 30$
- At least 40 units of Alanine: $3x_1 + 5x_2 \geq 40$
- At most 50 units of Methionine: $5x_1 + 2x_2 \leq 50$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## 3: Write down the optimization problem in symbolic form

The optimization problem can be written as:

Minimize $7x_1 + x_2$

Subject to:
- $2x_1 + 5x_2 \geq 30$
- $3x_1 + 5x_2 \geq 40$
- $5x_1 + 2x_2 \leq 50$
- $x_1 \geq 0$
- $x_2 \geq 0$

## 4: Convert the problem into a Gurobi code

```python
import gurobi

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

# Define the variables
x1 = model.addVar(name="orange_juice", lb=0)
x2 = model.addVar(name="apple_juice", lb=0)

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

# Add constraints
model.addConstr(2 * x1 + 5 * x2 >= 30, name="Lysine_constraint")
model.addConstr(3 * x1 + 5 * x2 >= 40, name="Alanine_constraint")
model.addConstr(5 * x1 + 2 * x2 <= 50, name="Methionine_constraint")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Glasses of orange juice: {x1.varValue}")
    print(f"Glasses of apple juice: {x2.varValue}")
    print(f"Objective: {model.objVal}")
else:
    print("No optimal solution found.")
```

## 5: Provide the symbolic representation in the required format

```json
{
    'sym_variables': [('x1', 'glasses of orange juice'), ('x2', 'glasses of apple juice')],
    'objective_function': '7*x1 + x2',
    'constraints': [
        '2*x1 + 5*x2 >= 30',
        '3*x1 + 5*x2 >= 40',
        '5*x1 + 2*x2 <= 50',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```