## Step 1: Define the symbolic representation of the problem
Let's denote the number of cans of Soda 1 to buy as $x_1$ and the number of cans of Soda 2 to buy as $x_2$. The objective is to minimize the total cost, which is $5x_1 + 7x_2$. The constraints are:
- Consume at least 50 units of caffeine: $3x_1 + 2x_2 \geq 50$
- Consume at least 40 units of sugar: $2x_1 + 5x_2 \geq 40$
- $x_1 \geq 0$ and $x_2 \geq 0$ since the number of cans cannot be negative.

## 2: Express the problem in the required symbolic format
The symbolic variables are:
- $x_1$ for Soda 1
- $x_2$ for Soda 2

The objective function to minimize is: $5x_1 + 7x_2$

The constraints are:
- $3x_1 + 2x_2 \geq 50$
- $2x_1 + 5x_2 \geq 40$
- $x_1 \geq 0$
- $x_2 \geq 0$

In the required format:
```json
{
'sym_variables': [('x1', 'Soda 1'), ('x2', 'Soda 2')],
'objective_function': '5*x1 + 7*x2',
'constraints': ['3*x1 + 2*x2 >= 50', '2*x1 + 5*x2 >= 40', 'x1 >= 0', 'x2 >= 0']
}
```

## 3: Convert the problem into Gurobi code
To solve this linear programming problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="Soda1", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="Soda2", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Set the objective function
    model.setObjective(5 * x1 + 7 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(3 * x1 + 2 * x2 >= 50, name="Caffeine_Constraint")
    model.addConstr(2 * x1 + 5 * x2 >= 40, name="Sugar_Constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Buy {x1.varValue} cans of Soda 1 and {x2.varValue} cans of Soda 2.")
        print(f"Total cost: ${5 * x1.varValue + 7 * x2.varValue:.2f}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```