## Step 1: Define the symbolic representation of the problem
The problem involves minimizing an objective function subject to several constraints. The variables are 'vincas', 'ferns', 'petunias', and 'daisies', which we can represent symbolically as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $7.59x_0 + 6.13x_1 + 2.19x_2 + 7.82x_3$.

## 3: List the constraints in symbolic notation
The constraints given are:
- $2.32x_1 + 4.7x_3 \geq 53$
- $7.69x_0 + 7.99x_2 \geq 57$
- $7.69x_0 + 4.7x_3 \geq 49$
- $7.69x_0 + 2.32x_1 + 7.99x_2 + 4.7x_3 \geq 49$
- $8.06x_0 + 4.74x_2 \geq 47$
- $1.47x_1 + 4.74x_2 + 3.12x_3 \geq 45$
- $8.06x_0 + 4.74x_2 + 3.12x_3 \geq 45$
- $1.47x_1 + 4.74x_2 + 3.12x_3 \geq 53$
- $8.06x_0 + 4.74x_2 + 3.12x_3 \geq 53$
- $8.06x_0 + 1.47x_1 + 4.74x_2 + 3.12x_3 \geq 53$
- $7.69x_0 + 4.7x_3 \leq 80$
- $2.32x_1 + 7.99x_2 + 4.7x_3 \leq 69$
- $7.69x_0 + 7.99x_2 + 4.7x_3 \leq 102$
- $7.69x_0 + 2.32x_1 + 4.7x_3 \leq 173$
- $1.47x_1 + 3.12x_3 \leq 211$
- $8.06x_0 + 1.47x_1 \leq 97$
- $4.74x_2 + 3.12x_3 \leq 92$
- $1.47x_1 + 4.74x_2 \leq 200$
- $8.06x_0 + 4.74x_2 \leq 69$

## 4: Define the symbolic representation of the problem in JSON format
```json
{
    'sym_variables': [
        ('x0', 'vincas'), 
        ('x1', 'ferns'), 
        ('x2', 'petunias'), 
        ('x3', 'daisies')
    ], 
    'objective_function': '7.59*x0 + 6.13*x1 + 2.19*x2 + 7.82*x3', 
    'constraints': [
        '2.32*x1 + 4.7*x3 >= 53',
        '7.69*x0 + 7.99*x2 >= 57',
        '7.69*x0 + 4.7*x3 >= 49',
        '7.69*x0 + 2.32*x1 + 7.99*x2 + 4.7*x3 >= 49',
        '8.06*x0 + 4.74*x2 >= 47',
        '1.47*x1 + 4.74*x2 + 3.12*x3 >= 45',
        '8.06*x0 + 4.74*x2 + 3.12*x3 >= 45',
        '1.47*x1 + 4.74*x2 + 3.12*x3 >= 53',
        '8.06*x0 + 4.74*x2 + 3.12*x3 >= 53',
        '8.06*x0 + 1.47*x1 + 4.74*x2 + 3.12*x3 >= 53',
        '7.69*x0 + 4.7*x3 <= 80',
        '2.32*x1 + 7.99*x2 + 4.7*x3 <= 69',
        '7.69*x0 + 7.99*x2 + 4.7*x3 <= 102',
        '7.69*x0 + 2.32*x1 + 4.7*x3 <= 173',
        '1.47*x1 + 3.12*x3 <= 211',
        '8.06*x0 + 1.47*x1 <= 97',
        '4.74*x2 + 3.12*x3 <= 92',
        '1.47*x1 + 4.74*x2 <= 200',
        '8.06*x0 + 4.74*x2 <= 69'
    ]
}
```

## 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="vincas", vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="ferns", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="petunias", vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name="daisies", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(7.59*x0 + 6.13*x1 + 2.19*x2 + 7.82*x3, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(2.32*x1 + 4.7*x3 >= 53)
    model.addConstr(7.69*x0 + 7.99*x2 >= 57)
    model.addConstr(7.69*x0 + 4.7*x3 >= 49)
    model.addConstr(7.69*x0 + 2.32*x1 + 7.99*x2 + 4.7*x3 >= 49)
    model.addConstr(8.06*x0 + 4.74*x2 >= 47)
    model.addConstr(1.47*x1 + 4.74*x2 + 3.12*x3 >= 45)
    model.addConstr(8.06*x0 + 4.74*x2 + 3.12*x3 >= 45)
    model.addConstr(1.47*x1 + 4.74*x2 + 3.12*x3 >= 53)
    model.addConstr(8.06*x0 + 4.74*x2 + 3.12*x3 >= 53)
    model.addConstr(8.06*x0 + 1.47*x1 + 4.74*x2 + 3.12*x3 >= 53)
    model.addConstr(7.69*x0 + 4.7*x3 <= 80)
    model.addConstr(2.32*x1 + 7.99*x2 + 4.7*x3 <= 69)
    model.addConstr(7.69*x0 + 7.99*x2 + 4.7*x3 <= 102)
    model.addConstr(7.69*x0 + 2.32*x1 + 4.7*x3 <= 173)
    model.addConstr(1.47*x1 + 3.12*x3 <= 211)
    model.addConstr(8.06*x0 + 1.47*x1 <= 97)
    model.addConstr(4.74*x2 + 3.12*x3 <= 92)
    model.addConstr(1.47*x1 + 4.74*x2 <= 200)
    model.addConstr(8.06*x0 + 4.74*x2 <= 69)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Vincas: ", x0.varValue)
        print("Ferns: ", x1.varValue)
        print("Petunias: ", x2.varValue)
        print("Daisies: ", x3.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```