## Symbolic Representation

The problem can be converted into a symbolic representation as follows:

Let's define the variables:
- $x_1$ as the number of scoops of Alpha
- $x_2$ as the number of scoops of Beta

The objective is to minimize the cost, which can be represented as: $1x_1 + 2x_2$

The constraints based on the problem description are:
- $5x_1 + 10x_2 \geq 50$ (at least 50 grams of iron)
- $20x_1 + 3x_2 \geq 40$ (at least 40 grams of biotin)
- $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints, as the number of scoops cannot be negative)

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'scoops of Alpha'), ('x2', 'scoops of Beta')],
    'objective_function': '1*x1 + 2*x2',
    'constraints': [
        '5*x1 + 10*x2 >= 50',
        '20*x1 + 3*x2 >= 40',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Iron_Biotin_Optimization")

# Define the variables
x1 = model.addVar(name="Alpha", lb=0)  # scoops of Alpha
x2 = model.addVar(name="Beta", lb=0)   # scoops of Beta

# Define the objective function
model.setObjective(1*x1 + 2*x2, gp.GRB.MINIMIZE)

# Define the constraints
model.addConstr(5*x1 + 10*x2 >= 50, name="Iron_Requirement")
model.addConstr(20*x1 + 3*x2 >= 40, name="Biotin_Requirement")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Scoops of Alpha: {x1.varValue}")
    print(f"Scoops of Beta: {x2.varValue}")
    print(f"Total Cost: {model.objVal}")
else:
    print("The model is infeasible or unbounded.")
```