## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the acres of pumpkins
- $x_2$ represents the acres of potatoes

## Step 2: Formulate the objective function
The revenue per acre of pumpkins is $150 and the revenue per acre of potatoes is $200. The objective function to maximize revenue is:
\[ \text{Maximize:} \quad 150x_1 + 200x_2 \]

## 3: Define the constraints for equipment usage
Each equipment can be used for 12 hours a day divided in any way between the two farms.

- Tractor constraint: $0.5x_1 + 0.9x_2 \leq 12$
- Plow constraint: $0.6x_1 + 0.5x_2 \leq 12$
- Combine constraint: $0.4x_1 + 0.3x_2 \leq 12$

## 4: Non-negativity constraints
- $x_1 \geq 0$ (acres of pumpkins cannot be negative)
- $x_2 \geq 0$ (acres of potatoes cannot be negative)

## 5: Symbolic representation
The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'acres of pumpkins'), ('x2', 'acres of potatoes')],
    'objective_function': '150*x1 + 200*x2',
    'constraints': [
        '0.5*x1 + 0.9*x2 <= 12',
        '0.6*x1 + 0.5*x2 <= 12',
        '0.4*x1 + 0.3*x2 <= 12',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 6: Gurobi code
Now, let's implement this problem using Gurobi in Python:
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="pumpkin_acres", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="potato_acres", lb=0, ub=gurobi.GRB.INFINITY)

    # Set objective function
    model.setObjective(150 * x1 + 200 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(0.5 * x1 + 0.9 * x2 <= 12, name="tractor_constraint")
    model.addConstr(0.6 * x1 + 0.5 * x2 <= 12, name="plow_constraint")
    model.addConstr(0.4 * x1 + 0.3 * x2 <= 12, name="combine_constraint")

    # Optimize model
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Acres of pumpkins: {x1.varValue}")
        print(f"Acres of potatoes: {x2.varValue}")
        print(f"Max revenue: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_farming_equipment_allocation()
```