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

Let's denote the units of mix A and mix B as $x_1$ and $x_2$ respectively. The objective is to minimize the cost, which is $1x_1 + 1.25x_2$. The constraints based on the requirements are:

- Cement: $5x_1 + 6x_2 \geq 70$
- Sand: $2x_1 + x_2 \geq 20$
- Gravel: $x_1 + 2x_2 \geq 15$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Step 2: Convert the problem into a symbolic representation

The symbolic representation can be written as:
```json
{
'sym_variables': [('x1', 'units of mix A'), ('x2', 'units of mix B')],
'objective_function': '1*x1 + 1.25*x2',
'constraints': [
    '5*x1 + 6*x2 >= 70',
    '2*x1 + x2 >= 20',
    'x1 + 2*x2 >= 15',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## Step 3: Write the Gurobi code to solve the optimization problem

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0, ub=float('inf'), obj=1)  # units of mix A
    x2 = model.addVar(name="x2", lb=0, ub=float('inf'), obj=1.25)  # units of mix B

    # Add constraints
    model.addConstr(5 * x1 + 6 * x2 >= 70, name="cement_constraint")  # cement constraint
    model.addConstr(2 * x1 + x2 >= 20, name="sand_constraint")  # sand constraint
    model.addConstr(x1 + 2 * x2 >= 15, name="gravel_constraint")  # gravel constraint

    # Set the model to minimize
    model.setObjective(x1 + 1.25 * x2, gurobi.GRB.MINIMIZE)

    # Optimize the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Units of mix A: {x1.varValue}")
        print(f"Units of mix B: {x2.varValue}")
        print(f"Total cost: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_concrete_mix_problem()
```