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

## Step 2: Formulate the objective function
The objective is to maximize revenue. Given that the revenue per acre of daikons is $300 and the revenue per acre of fennels is $250, the objective function can be written as:
\[ \text{Maximize:} \quad 300x_1 + 250x_2 \]

## 3: Define the constraints
1. The farmer has 300 acres of land available:
\[ x_1 + x_2 \leq 300 \]
2. The farmer has 500 hours available for watering, with daikons requiring 0.5 hours/acre and fennels requiring 1.5 hours/acre:
\[ 0.5x_1 + 1.5x_2 \leq 500 \]
3. The farmer has $7400 available for compost, with daikons requiring $70/acre and fennels requiring $50/acre:
\[ 70x_1 + 50x_2 \leq 7400 \]
4. Non-negativity constraints:
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

## 4: Symbolic Representation
The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'acres of daikons'), ('x2', 'acres of fennels')],
    'objective_function': '300*x1 + 250*x2',
    'constraints': [
        'x1 + x2 <= 300',
        '0.5*x1 + 1.5*x2 <= 500',
        '70*x1 + 50*x2 <= 7400',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

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

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

    # Define the variables
    x1 = model.addVar(lb=0, name="daikons")
    x2 = model.addVar(lb=0, name="fennels")

    # Set the objective function
    model.setObjective(300*x1 + 250*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(x1 + x2 <= 300, name="land_constraint")
    model.addConstr(0.5*x1 + 1.5*x2 <= 500, name="watering_constraint")
    model.addConstr(70*x1 + 50*x2 <= 7400, name="compost_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Acres of daikons: {x1.varValue}")
        print(f"Acres of fennels: {x2.varValue}")
        print(f"Max Revenue: {model.objVal}")
    else:
        print("The problem is infeasible")

solve_organic_farming_problem()
```