## Step 1: Define the symbolic representation of the problem
Let's denote the hours of running plant Gamma as $x_1$ and the hours of running plant Delta as $x_2$. The objective is to minimize the cost, which can be represented as $35x_1 + 95x_2$. The constraints are:
- $4x_1 + 6x_2 \geq 90$ (to meet the demand for asphalt)
- $3x_1 + 5x_2 \geq 85$ (to meet the demand for bricks)
- $x_1 \geq 0$ and $x_2 \geq 0$ (since the hours of running plants cannot be negative)

## 2: Convert the problem into a symbolic representation
The symbolic variables are:
- $x_1$ for the hours of running plant Gamma
- $x_2$ for the hours of running plant Delta

The objective function is: $35x_1 + 95x_2$

The constraints are:
- $4x_1 + 6x_2 \geq 90$
- $3x_1 + 5x_2 \geq 85$
- $x_1 \geq 0$
- $x_2 \geq 0$

## 3: Represent the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'hours of running plant Gamma'), ('x2', 'hours of running plant Delta')],
    'objective_function': '35*x1 + 95*x2',
    'constraints': ['4*x1 + 6*x2 >= 90', '3*x1 + 5*x2 >= 85', 'x1 >= 0', 'x2 >= 0']
}
```

## 4: Implement the problem in Gurobi code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='Gamma_hours', lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name='Delta_hours', lb=0, ub=gurobi.GRB.INFINITY)

    # Define the objective function
    model.setObjective(35 * x1 + 95 * x2, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(4 * x1 + 6 * x2 >= 90, name='asphalt_demand')
    model.addConstr(3 * x1 + 5 * x2 >= 85, name='bricks_demand')

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'Hours of running plant Gamma: {x1.x}')
        print(f'Hours of running plant Delta: {x2.x}')
        print(f'Total cost: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_production_planning()
```