## Step 1: Define the symbolic representation of the problem
Let's denote the number of servings of beans as $x_1$ and the number of servings of cereal as $x_2$. The objective is to minimize the cost, which is $2x_1 + x_2$. The constraints are:
- Carbohydrate requirement: $50x_1 + 30x_2 \geq 300$
- Protein requirement: $20x_1 + 5x_2 \geq 150$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## 2: Convert the problem into a symbolic representation
The symbolic representation can be written as:
```json
{
    'sym_variables': [('x1', 'servings of beans'), ('x2', 'servings of cereal')],
    'objective_function': '2*x1 + x2',
    'constraints': [
        '50*x1 + 30*x2 >= 300',
        '20*x1 + 5*x2 >= 150',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 3: Write the Gurobi code to solve the problem
We will use the Gurobi Python API to formulate and solve the linear programming problem.

```python
import gurobi

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

# Define the variables
x1 = model.addVar(name='x1', lb=0, vtype=gurobi.GRB.CONTINUOUS)  # servings of beans
x2 = model.addVar(name='x2', lb=0, vtype=gurobi.GRB.CONTINUOUS)  # servings of cereal

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

# Define the constraints
model.addConstr(50*x1 + 30*x2 >= 300, name='carbohydrate_requirement')
model.addConstr(20*x1 + 5*x2 >= 150, name='protein_requirement')

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print(f"Optimal servings of beans: {x1.varValue}")
    print(f"Optimal servings of cereal: {x2.varValue}")
    print(f"Minimum cost: {model.objVal}")
else:
    print("The problem is infeasible")
```